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Skin Cancer and Melanoma Prevention (84) mHealth for Wellness, Behavior Change and Prevention (2206)
Published on 11.7.2019 in Vol 2 , No 1 (2019) :2019
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13376, first published January 14, 2019 .
Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise
Tiago M de Carvalho 1 ; Eline Noels 1 ; Marlies Wakkee 1 ; Andreea Udrea 2 ; Tamar Nijsten 1
Tiago M de Carvalho1, PhD ;
Eline Noels1, MD ;
Marlies Wakkee1, MD, PhD ;
Andreea Udrea2, PhD ;
Tamar Nijsten1, MD, PhD
1Department of Dermatology, Erasmus Medical Center, Rotterdam, Netherlands
2Department of Automatic Control and System Engineering, Polytechnic University of Bucharest, Bucharest, Romania
Tiago M de Carvalho, PhD
Erasmus Medical Center
Doctor Molewaterplein 40
Rotterdam, 3015 GD
Phone: 31 649331937
Email: t.decarvalho@erasmusmc.nl
Skin cancer is a growing public health problem. Early and accurate detection is important, since prognosis and cost of treatment are highly dependent on cancer stage at detection. However, access to specialized health care professionals is not always straightforward, and population screening programs are unlikely to become implemented. Furthermore, there is a wide margin for improving the efficiency of skin cancer diagnostics. Specifically, the diagnostic accuracy of general practitioners and family physicians in differentiating benign and malignant skin tumors is relatively low. Both access to care and diagnostic accuracy fuel interest in developing smartphone apps equipped with algorithms for image analyses of suspicious lesions to detect skin cancer. Based on a recent review, seven smartphone apps claim to perform image analysis for skin cancer detection, but as of October 2018, only three seemed to be active. These apps have been criticized in the past due to their lack of diagnostic accuracy. Here, we review the development of the SkinVision smartphone app, which has more than 900,000 users worldwide. The latest version of the SkinVision app (October 2018) has a 95% sensitivity (78% specificity) for detection of skin cancer. The current accuracy of the algorithm may warrant the use of this app as an aid by lay users or general practitioners. Nonetheless, for mobile health apps to become broadly accepted, further research is needed on their health impact on the health system and the user population. Ultimately, mobile health apps could become a powerful tool to reduce health care costs related to skin cancer management and minimize the morbidity of skin cancer in the population.
skin cancer; early detection; mhealth; smartphone app; mobile phone
Rationale for Using Mobile Health Apps for Early Detection of Skin Cancer
There are three main types of skin cancers—malignant melanoma (MM), squamous cell carcinoma (SCC), and basal cell carcinoma (BCC)—with the latter two also known as keratinocyte carcinoma (KC). In the United States, it was estimated that about 91,000 people will be diagnosed with melanoma and 9300 will die due to MM in 2018 [1]; in addition, in 2012, more than 5 million people were diagnosed and 3 million received treatment for KC, which is more than the values for all other cancers combined [2]. Globally, in 2015, there were about 351,000 new incident melanoma cases and 60,000 melanoma-related deaths [3], with the highest burden of disease in Australasia, North America, and Europe. In the last 30 years, the incidence of MM, adjusted for changes in the age distribution of the population, more than doubled in the United States (among Caucasians) and the United Kingdom; nearly doubled in Norway, Sweden, and New Zealand; and increased by approximately 75% in Australia [4]. This is mostly due to changes in risk factors such as increased exposure to ultraviolet light and indoor tanning [4-6]. Since these risk factors are mostly preventable, comprehensive prevention programs aimed at better sun protection behavior have been implemented in several countries, such as SunSmart in Australia [7].
Although several organizations have issued recommendations on how often to check skin lesions for individuals at higher risk (eg, Fitzpatrick scale I-III, a family history of melanoma, a history of sun-damaged skin, and multiple atypical nevi), ranging from every 3 months to every year [8], most countries do not have an organized early detection program for skin cancer. The US Preventive Services Task Force has issued an I-recommendation for skin cancer screening [9], indicating that there is insufficient evidence to evaluate the harms and benefits of skin cancer screening. Currently, there are only two major skin screening programs: (1) In the United States, the American Academy of Dermatology, which started in 1985, includes screening and skin cancer awareness education [10] and (2) in Germany, a national screening program was started in 2008 [11]; the program in Germany does not seem to be effective in reducing skin cancer–related mortality and morbidity [12].
In practice, it is difficult to provide a high-quality skin checks, even for high-risk individuals. Waiting times and, in some areas, dermatologist shortages, out-of-pocket costs, and distance to the nearest dermatologist [13] may discourage people at risk from receiving dermatological care. For example, in the United States, a study found that availability of a dermatologist within the county is associated with a 35% decrease in melanoma mortality [14]. Another US estimate found that only a quarter of individuals at higher risk of skin cancer have ever received a total skin body examination [8].
In several countries, namely, in the United Kingdom and the Netherlands, skin checks are first carried out by a general practitioner (GP, also sometimes referred to as primary care provider), who may then choose to refer a patient to a dermatologist if there is a suspicion of skin cancer. However, several studies suggest that the accuracy of GPs to detect skin cancer is relatively low [15-20]. The sensitivity of GPs without specific training to detect skin cancer was estimated to be below 60% in British and Dutch studies [15,16]. One US study found that only 35% of patients had a correct diagnosis [17]. Altogether, this may result in a delay in diagnosis or missing the cancer in its earlier stages when patient survival is more favorable and treatment is less costly. Furthermore, many GP consultations and subsequent referrals to a specialist to examine the skin for cancer result in a benign diagnosis. A Dutch study found that 69% of GP consultations related to suspicious skin lesions result in a benign diagnosis [19], and two separate studies in the Netherlands estimated that a large proportion (40%) of referral cases to the dermatologist due to suspicion of skin cancer turned out to be benign cases [19,20]. Two studies (in the United States and Germany) including more than 70 dermatologists found that dermatologists' disease classification decisions have a specificity of 60%-80% [21,22], which may result in unnecessary biopsies/excisions.
Early detection and surveillance of skin cancer could be more efficient with mobile health (mHealth) apps, which are easily accessible due to the ubiquity of smartphone usage. One example of a smartphone app for self-assessment of skin lesions for skin cancer is the SkinVision app (SVA), developed by SkinVision, BV, The Netherlands). In the next section, we review the development of SkinVision app over time.
Development of a Smartphone App for Skin Cancer Detection
SkinVision is a smartphone app built as a digital dermatology service for self-monitoring skin lesions. It was launched in 2011 and as of October 2018, it was on its fifth major version. The workflow of the app is given in Figure 1.
Figure 1. Workflow of the SkinVision app service. SVA: SkinVision app.
A user can self-assess the risk of a skin lesion for skin cancer by taking a photo with his/her smartphone, which is processed by an algorithm. The outcome of the procedure is a binary risk rating, which can be low or high. This smartphone app does not provide a diagnosis (eg, "you have melanoma"). For high-risk cases, the user receives advice from the costumer care team based on the image assessment of an in-house dermatologist.
Development of the SkinVision App Service
The history of the SkinVision app service is shown in Table 1. It went through several upgrades throughout its history, modifying the camera, the algorithm and its evaluation, type of lesions analyzed, and communication of the algorithm result to its user. One of the major initial challenges was related to image acquisition. In the beginning, there was no filter on the images sent for analyses, which meant that a significant proportion of the pictures taken by users was of insufficient quality to be analyzed by the disease classification algorithm or did not even contain a lesion to be analyzed. Since version 3 of the SkinVision app (2014), a special camera module [23] has been embedded, which only lets the camera take a photo after certain minimal quality conditions are met. Compared to unfiltered images taken with a standard smartphone camera, the camera module reduces the number of blurry photos by about 52% on an average (determined using 2018 data). Altogether, improvements in the camera module (namely, image quality checks) and the algorithm pipeline led to a reduction in the number of assessments that failed to produce a risk rating, from 26% in 2016 to 2% in 2018 on an average.
An overview of studies on the diagnostic accuracy of the SkinVision app is shown in Table 2. Diagnostic accuracy is evaluated based on two measures: sensitivity (proportion of lesions correctly classified as high risk) and specificity (the proportion of lesions correctly rated as benign). The first algorithm for skin lesion assessment was a rule-based fractal algorithm [24]. Initially, it was focused on pigmented skin lesions and only able to analyze whether MM was present in the lesion, and it was tested based on clinical review of images. The Munich University Hospital study was the first peer-reviewed publication where the SkinVision app algorithm was evaluated against histopathology [24], and thereafter, the algorithm achieved 73% sensitivity (83% specificity). During the Catharina Hospital Eindhoven study [25], the algorithm was recalibrated to analyze pigmented and nonpigmented lesions. Currently, it can detect several types of skin cancer (MM, SCC, and BCC) and skin conditions that can lead to skin cancer, namely, actinic keratosis and Bowen disease. It achieved 80% sensitivity (78% specificity) after inclusion of user clinical information. Although in the Eindhoven study, only 233 lesions were used for calibration, in 2018, the SkinVision app assembled a training dataset of more than 130,000 images that were risk classified by a dermatologist from the app's user database. This led to replacement of the rule-based classification algorithm by a machine learning approach (A Udrea et al, PhD, unpublished material, 2019).
Table 1. Development of the SkinVision smartphone app.
Version Launch date Algorithm Camera Type of skin lesion Type of skin cancer detected Testing
1 May 2011 Rule-based fractal algorithm version 1 Standard smartphone camera Pigmented skin lesion only Malignant melanoma Preclinical testing using 600 images against the opinion of two dermatologists
2 December 2012 Rule-based fractal algorithm version 2 Standard smartphone camera, exclusion criteria introduced Pigmented skin lesion only Malignant melanoma Preclinical testing using 600 images against the opinion of two dermatologists
3 September 2014 Rule-based fractal algorithm version 2 Camera module: exclusion criteria automated Pigmented skin lesion only Malignant melanoma Clinical study, Munich University Hospital
4 July 2016 Rule-based fractal algorithm version 3, all outcomes checked by dermatologists (Sept 2016) Camera module version 1 Pigmented and nonpigmented skin lesions Malignant melanoma, squamous cell carcinoma, basal cell carcinoma, some premalignant lesions Clinical study, Catharina Hospital Eindhoven (more types of skin cancer)
5 January 2018 Machine learning algorithm for image processing and classification Camera module major version 2a Pigmented and nonpigmented skin lesions Malignant melanoma, squamous cell carcinoma, basal cell carcinoma, some premalignant lesions Data from previous clinical studies and user database with new algorithm
aNew features include a dynamic grey threshold to differentiate between normal skin and lesion and a feature that prevents taking pictures without uniform luminosity.
Table 2. Studies on the accuracy of the SkinVision app's risk assessment. All studies presented here were sponsored by the SkinVision app.
Study Data Algorithm Test set Sensitivity (%) Specificity (%) Remarks
Maier et al, 2014 [24] University Hospital Munich Rule-based fractal algorithm version 2 26 lesions with melanoma 73 83 Algorithm tested only on malignant melanoma
Thissen et al, 2017 [25] Catharina Hospital Eindhoven Rule-based fractal algorithm version 3 108 lesions including several types of skin cancer 80a 78a Algorithm tested mostly on keratinocyte carcinoma (Munich data also used for testing)
Udrea et al, 2019b Clinical studies and SkinVision app user database Machine learning–based algorithm 285 lesions with skin cancer, from clinical studies and a user database with histopathology information 95 78 All types of skin cancer
aAfter incorporating answers into a questionnaire about the skin lesion.
bManuscript under peer review (June 2019). For more details on these results, see Multimedia Appendix 1.
SkinVision App Service in 2018
Before downloading the app, the smartphone should be equipped with a camera capable of producing a video stream with sufficiently high resolution. Although the app uses a regular smartphone camera, the camera module embedded in the app automatically places some restrictions to ensure minimal quality requirements of the images are met: The image needs to be focused, the lesion should be present and contained in the image, and there should be no hair or shadows covering the lesion. The module also prevents the camera from taking images that cannot be assessed by the algorithm (eg, lesions under a nail or near clothing in a skin fold).
Algorithm for Lesion Assessment
There are several steps to analyze the lesion. The first task of the algorithm is to identify and separate the lesion from normal skin. This is done using a machine learning technique called conditional Generative Adversarial Neural Network [26] (A Udrea et al, PhD, unpublished material, 2019). After the lesion is segmented, all "noise" (eg, hair surrounding the lesion) is removed in the image by applying an inpainting procedure [27]. The third step is to extract the features from the lesion that are used in the disease classification algorithm. These features include 24 shape, color, and texture attributes that characterize the lesion. A Support Vector Machines (SVM) classifier is used to provide a risk rating, which can be high or low. The SVM model is obtained by maximizing sensitivity to detect cancer subject to a constraint of a minimal specificity value (eg, 80%). The optimization is performed using a Particle Swarm Optimization algorithm [28]. The classification algorithm is regularly updated and retrained with new data. This is necessary to maintain robustness to variation due to imaging, newer devices, and the user population adopting the app.
Training and Testing
For training of the algorithm, we used images obtained from the user database (more than 130,000 pictures from 30,000 users), which received either a low- or high-risk tag during quality control of the algorithm by a dermatologist affiliated with SkinVision. A selection of cases clinically validated as low risk were randomly selected from the user database, while all cases rated as high risk or with a histopathological report were used, since there were considerably fewer of them. For testing the sensitivity, we used 285 skin lesions derived from both previous clinical studies (Munich and Catharina Hospital, 195 skin lesions, containing most common forms of skin cancer) and the user database (90 cases of melanoma that received histopathological confirmation from users). Furthermore, to test the specificity, we used 6000 randomly selected cases from the user database (June to August 2018), which were tagged as benign by SkinVision-affiliated dermatologists and were not used in training. An overview of these datasets together with the participant flowcharts are shown in Multimedia Appendix 1.
The gold standard (main comparator) to evaluate algorithm sensitivity is comparison against histopathologically validated cancers. The second comparator is the performance of the algorithm against the image assessment of the dermatologist (which is comparable to a teledermatology consult). In order to calculate the specificity, we use images of lesions that were classified by dermatologists as benign cases, since these are not usually biopsied, and therefore, there is no histopathology report. Sensitivity has improved from 73% in the first peer-reviewed study, where only MM was detectable, to 95% in the current version of the algorithm (78% specificity), where the SkinVision app can detect all forms of skin cancer (Table 2 and Multimedia Appendix 1).
Postassessment Follow-Up
Since 2016, images processed by the algorithm are reviewed by at least one affiliated dermatologist. To help users with the interpretation of high-risk cases, a senior dermatologist adds advice depending on the probable severity of the disease. The advice can contain the labels "Show," "Visit," or "Urgent." "Show" indicates that the lesion should be shown at the next planned doctor appointment, "Visit" indicates that the appointment should be made soon, and "Urgent" advises the user to show the lesion to a doctor as soon as possible. Users with a low-risk rating only receive a reminder to check their skin regularly.
Assessments with a high-risk rating given by the dermatologist are followed up by the customer support department of the SkinVision app. If the user does not respond, he/she may receive additional messages encouraging a visit to the doctor, depending on the perceived severity of the disease. Some users share their diagnosis of skin cancer with the SkinVision app (n=3806, Multimedia Appendix 1). Of these, a small proportion (338/3806, 8.8%) share the histopathology report. At the end of September 2018, about 338 users had shared histopathological reports, of which 58% (178/338) were of MM diagnosis. The histopathologically validated cases are used for training and testing the algorithm.
Smartphone App Users
In Table 3, we show self-reported demographic data on SkinVision app users. As of September 2018, the SkinVision app has performed more than 1.8 million assessments. Some of these users shared their demographic data with the SkinVision app: 56% (355,491/635,807) shared their age group and 28.5% (181,706/635,807) shared their gender. Although skin cancer is more prevalent in older age groups, only 7% of the people were older than 60 years of age (19,358/355,491) in the user database and about 31% (110,529/355,491) were younger than 30 years of age. More than 60% of the users were female (118,182/181,706). A majority of the users come from the following countries: The Netherlands (n=111,063, 17.4%), United Kingdom (n=109,178, 17.2%), Australia (n=109,126, 17.2%), New Zealand (n=70,244, 11%), and Belgium (n=21,328, 3.3%).
Table 3. Self-reported demographic characteristics of the SkinVision app users. The data are from the SkinVision app proprietary user database, accessed in September 2018. Numbers are based on users, who made a picture that was evaluated by the algorithm, and filled the online questionnaire.
Characteristic Users
Number of registered users 931,789
Total number of users with an assessment 635,807a
Genderb, n (%)
Male 62,914 (9.9)
Female 118,182 (18.6)
Missing 454,731 (71.5)
Age groupb (years), n (%)
<30 110,529 (17.4)
30-39 98,327 (15.5)
50-59 46,840 (7.4)
>70 5509 (0.9)
Country, n (%)
The Netherlands 111,063 (17.4)
Australia 109,178 (17.2)
United Kingdom 109,126 (17.2)
New Zealand 70,244 (11.0)
Belgium 21,128 (3.3)
Others 215,355 (33.9)
aSome users may be health care providers taking pictures of multiple patients, so this is likely an underestimate.
bFor the gender and age categories, about 75% and 44%, respectively, did not fill any data.
State of the Field
Available Mobile Health Apps for Skin Cancer Detection
A recent review [29] found that there are 43 smartphone apps developed for skin cancer detection, monitoring, and education. Of these, nine smartphone apps use an algorithm for image analysis [29]. We verified the current status of these smartphone apps in December 2018 with Google search and PubMed and in app stores. The results are presented in Table 4. We confirm that seven apps claim to use an algorithm for image analysis. Of these, four do not seem to be active as of October 2018. Compared to a previous review conducted in July 2014 [30], there are now less apps available for risk assessment of skin lesions through image analysis (three instead of four).
Table 4. List of smartphone apps that claim to perform skin lesion image analyses to detect skin cancer, based on a systematic review [29].a
Commercial name Algorithmb Evidence on PubMed Status Sourcec
DermaCompare Machine learning Not found Removed from app store, last update on January 2017 [31]
Lubax Content-based image retrieval, k-nearest neighbor Yes, one supported peer-reviewed publicationd Removed from app store, last update on February 2015 [32,33]
MSkinDoctore Grab cut algorithm (segmentation) and SVMf (classification) Not found; there is a conference abstract only Removed from app store, update on February 2016 [34]
MySkinMap Machine learning Not found Removed from app store, last update on September 2016 [35]
SkinScan Image processing techniques, ABCDE rule Unclearg Available [36]
SkinVision Conditional generative adversarial neural network (segmentation) and SVM (classification) Yes, two supported peer-reviewed publications, evaluated in independent publications Available [23-25,37], A Udrea et al, PhD, unpublished material, 2019
SpotMole Image processing techniques, ABCDE rule Yes, evaluated in independent publications Availableh [38]
aAfter verifying the websites of every app (if available), it seems that two of the apps mentioned in the Ngoo et al study [29] with commercial names—Myskinpal and Skin Prevention - Photo Body—do not claim to perform automated image analysis for risk assessment. They only store images of moles to track changes.
bIf available, the information is retrieved from scientific publications; otherwise, it is collected from the company's own website or app store description.
cAccessed on Dec 12, 2018.
dResults obtained in this publication [32] only for melanomas and large lesions.
eThere is another app available with the same name; however, that one does not perform image analyses.
fSVM: support vector machines.
gThere is an associated reference to an app of the same name from 2011; however, this does not appear to be the same app.
hThis smartphone app has a website (spotmole.com); however, it was offline at the last time of access (Dec 12, 2018). It is unclear if this project is still alive, given the fact that the last update was in March 2016 and that it seems this app is being developed by a single individual.
Comparison of the SkinVision App With Other Apps
Currently, there seem to be three apps available for detection of skin cancer, including SkinVision app, SpotMole, and skinScan. All three allow the user to take a picture with the smartphone camera. The SkinVision app algorithm is based on machine learning techniques, while SpotMole and skinScan use algorithms inspired by the ABCDE rule [39]. The SkinVision app involves quality control by a dermatologist; however, the other apps do not seem to offer any further follow-up or advice to users.
Tables 2 and 5 show the diagnostic accuracy results from recent publications. We found five peer-reviewed studies and one submitted study about two available mHealth apps and two mHealth apps that do not exist anymore. Of the three currently available apps, no diagnostic accuracy or other studies were found for skinScan. The other two smartphone apps were evaluated in at least one study [40], and only one app (ie, SkinVision app) [24,25] has published evidence to show whether their proprietary algorithm is accurate.
mHealth apps for skin cancer assessment (including SkinVision app) have been criticized in past studies [40,41], because their accuracy was found to be significantly lower than that of a dermatologist. In Tables 2 and 5, only three studies showed a diagnostic accuracy close to that of a dermatologist, and one of these studies [32] only showed a high accuracy for large melanoma lesions. Although some of these studies are recent, these findings are possibly already outdated, as this is a rapidly evolving field. These results could also be explained by the limited sample size, including too few skin cancer cases and selected samples, which may be inadequate to calculate sensitivity or specificity or, in the case of SkinVision app, nonutilization of the full service with the dermatologists' advice.
Overall, the amount of evidence on the diagnostic accuracy of smartphone apps is still scarce, as there are few mHealth apps providing this service. It is also difficult to make an accurate comparison between different apps, since the rate of service or algorithm change is faster than the process of peer-review publication. It could also be the case that some developers may choose to publish their results in sources that are not referenced in PubMed, namely, ArXiv. An illustrative example of these difficulties is a Cochrane review [42] published in December 2018 on the diagnostic accuracy of smartphone apps, which only found two studies but only included articles published before August 2016, making it possibly obsolete at the time of publication. For these reasons, one should be cautious when interpreting the available literature.
Table 5. Recent studies on the diagnostic accuracy of smartphone apps for risk assessment of skin lesions.
App, study, year Location for data collection Algorithm Test Set Sensitivity (95% CI) Specificity (95% CI) Remarks
SkinVision app, Ngoo et al, 2018 [40] Princess Alexandra Hospital, Brisbane Rule-based fractal algorithm version 2 1 malignant melanoma, 41 clinically suspicious lesionsa iOS: 57% (41-73), Android: 72% (58-87)b iOS: 50% (22-78), Android: 27% (1-56)b Only 1 malignant melanoma was found
SpotMole, Ngoo et al, 2018 [40] Princess Alexandra Hospital, Brisbane Algorithm based on the ABCDE rule 1 malignant melanoma, 41 clinically suspicious lesionsa 43% (28-58) 80% (60-100) Only 1 malignant melanoma was found
Dr Mole, Ngoo et al, 2018 [40] Princess Alexandra Hospital, Brisbane Algorithm based on the ABCDE rule 1 malignant melanoma, 41 clinically suspicious lesionsa 21% (9-34) 100% (100-100) Only 1 malignant melanoma was found
Lubax, Cheng et al, 2015 [32] DermNetNZ, New Zealand, and Los Angelesc Content-based image retrieval 208 lesions with melanomad 90% (86-94) 92% (85-95) Algorithm tested only on malignant melanoma (large lesions only)
Not reported, Dorairaj et al, 2017 [41]e Galway University Hospital Not reported 9 malignant melanomas 80% (52-96) 9% (0-41) Algorithm tested only on malignant melanoma
aAll lesions had a benign final histopathology diagnosis with the exception of one melanoma in situ.
bNgoo et al 2017 [40] reported the results per type of operating system: iOS/Android.
cDermNetZN is a publicly accessible skin lesion image database from New Zealand containing about 20,000 images. Images collected within the Los Angeles county were collected by the app company. No reference to a clinical site of the data collection was given in the publication.
dAlgorithm was only tested on "large lesions" defined as melanomas with a diameter ≥10 mm.
eDespite the study being published in 2017, the study took place in 2012.
Improving the Diagnostic Accuracy of Mobile Health Apps
A promising avenue to improve the diagnostic accuracy of mHealth apps is to train machine learning algorithms on large databases of skin cancer images. Several algorithms for skin cancer classification were recently developed based on clinical or dermoscopic images, with algorithm accuracy routinely on par with a dermatologist [21,22,43]. For mHealth apps, the task of skin lesion classification is more difficult, as the images are taken by the users themselves, with variability in angle, luminosity, and smartphone model. The SkinVision app showed that skin lesion classification based on smartphone images can also achieve high accuracy (Table 2; A Udrea et al, PhD, unpublished material, 2019).
Alternatives to Mobile Health Apps
Early detection of skin cancer could be significantly improved by launching a population screening program, but this is unlikely to become common due to the high costs and lack of evidence on harms and benefits [9,44]. As the main risk factors for skin cancer like indoor tanning or ultraviolet exposure are, in large part, preventable [45], primary prevention and awareness campaigns (eg, Melanoma Monday and SunSmart in Australia) could have a better cost-benefit ratio than early detection [46]. These campaigns are a way for the general public to proactively adopt preventive behaviors and possibly learn how to recognize suspicious skin lesions [7]; they seemed to have resulted in better sun protection behavior [47]. On the other hand, this success can be reversed if these awareness efforts are not continuous [47] and they do not solve the shortages or difficulties in access to high-quality skin checks.
Training GPs or nurses with a special interest in recognizing skin cancer increases the capacity for early and accurate detection. However, compared to mHealth apps, it still requires face-to-face contact, and it is likely not enough to address all needs [13,48]. Store-and-forward teledermatology [49] allows users to take a photo and have it analyzed remotely by a dermatologist. This may solve some of the problems with access to care, but is solely based on a clinical assessment of a health care professional and is thus not automated. Smartphone apps with good performance are likely to be more efficient and could lead to larger cost savings for the health system compared to the above mentioned alternatives.
Usability Risks of Smartphone Apps
Smartphone apps pose some risks for the user, specifically, if the algorithm returns a negative result while the user has cancer, and detection and treatment of skin cancer are delayed. It is very challenging to study the rate of false-negatives due to a lack of histological verification. The user may also fail to assess all relevant skin lesions, in particular, if they are located in places that are hard to reach or that the user cannot see. Given that the specificity of SkinVision app is about 80%, there will be a few false-positive cases. This may cause unnecessary stress on users or unnecessary visits to the GP/dermatologist. Finally, the user may not follow the advice given in the smartphone app due to a lack of trust or unawareness.
Evaluating the Health Impact of Mobile Health Apps
Impact of Mobile Health Apps on Health Care Costs
A Dutch study based on national claims data observed an increase of about 67% in skin cancer–related costs between 2007 and 2017 (E. Noels, MD, unpublished data, 2019). This is due to higher costs of skin cancer treatments, for example, newly available expensive targeted immunotherapies for late-stage melanomas and, to a lesser extent, due to an increase in the skin cancer incidence. mHealth apps for self-assessment of skin lesions could limit this cost increase by (1) by detecting cancers early, which will reduce the average cost of treatment (ie, less advanced disease) and recovery and (2) reducing the need for doctor visits, since many primary care (GP)–related consultations either result in a benign diagnosis or in referrals to a specialist of cases that are later diagnosed as benign [19].
Impact of Mobile Health Apps on Public Health
Easy access to a high-quality assessment of skin lesions may lead to detection of skin cancers at an earlier stage, when their prognosis and treatment are more favorable. On the other hand, this could also cause overdiagnosis and overtreatment. Currently, evidence on the benefits and harms of skin cancer screening is insufficient [9,44]. To date, there are no randomized skin cancer screening trials, and it is unlikely that there will be new trials launched in the near future, since they would require a substantial number of patients and a long follow-up and it would be difficult and possibly unethical to guarantee that people in the control group would not access skin cancer detection methods. Consequently, it is difficult to determine whether early detection of skin cancer reduces skin cancer specific mortality. Another important target outcome could be the incidence of advanced melanoma. Therefore, indirect evidence on harms and benefits could be obtained by comparing the stage distribution of cancers detected early with a smartphone app from national registries.
Implementation of Mobile Health Apps in the Health System
The health impact of mHealth apps also depends on where it is implemented, that is, whether it is restricted to health care professionals such as GPs or dermatologists or accessible to the lay population. Offering apps directly to lay users could result in significantly greater efficiency gains for the health system; however, some regulatory bodies may prefer to restrict the usage to health care professionals to minimize usability risks. The regulatory framework is evolving quickly, with the National Institute for Health and Care Excellence in the United Kingdom suggesting a comprehensive approach to regulate mHealth technologies, taking into account not only the safety and efficacy of the app, which can be shown by carrying out a diagnostic accuracy study, but also whether it can plausibly improve current health care pathways, acceptability with users, and cost-effectiveness compared to usual care [50].
Barriers to Access of Mobile Health Apps
After implementation, the health impact of mHealth apps will also depend on the persistence of barriers to adoption among users (either lay persons or care providers). Zhao et al [51] described a technology acceptance model for mobile health [51]. For lay users, age can play a role in the rate of adoption [51]. Middle-aged and older users (the ones who are at a higher risk of skin cancer) may give more importance to the perceived amount of effort needed to learn how to use the smartphone app and the perceived personal risk for skin cancer. For clinicians, we believe the perceived ease of use also plays an important role, since clinicians have a limited amount of time. Other important factors may include perceived usefulness and efficacy of the smartphone apps, namely, whether clinicians believe in the quality of the app and whether they believe it provides the necessary information to make a clinical decision.
Postmarket Surveillance of Mobile Health Apps
A key point for mHealth apps for skin cancer detection consists of performing appropriate market surveillance activities in order to minimize usability risks, since data based on clinical studies in a controlled setting are likely not sufficient to control for differences in image-taking behavior or characteristics of the smartphone model. Algorithms used in mHealth apps should then be updated periodically, given the feedback from its users, whether they are lay users or clinicians. It is not easy to have complete follow-up from users, since due to privacy reasons, it is not straightforward for the smartphone apps to obtain access to the final clinical or histopathological diagnosis after the lesion is assessed by the algorithm.
Research is still needed to establish the societal value of mHealth apps. First, there remains a need for more high-quality studies on their diagnostic accuracy in different populations. Second, given that these smartphone apps are accurate enough to be used by laypersons and GPs, its health and cost effects are yet to be evaluated.
The impact on the health system in terms of cost reduction due to less skin lesion–related visits still needs to be tested, ideally with a randomized control trial (RCT) accompanied by a cost-effectiveness analysis. However, performing an RCT may prove difficult. To design a trial capable of detecting a difference in the number of doctor visits, the sample size needed to carry out such a study is in the thousands, as shown by the Dutch data from 2010 [52] suggesting that about 93 consultations for every 1000 patients are related to skin cancer. The main problem is that this type of RCT has a high risk of contamination in the control group (no smartphone app), since access to smartphone apps and their usage is relatively simple. An alternative solution could be to follow a quasi-experimental approach for the design of the study [53].
In the absence of large RCTs and long-term follow-up data, modelling could be used to estimate the harms and benefits of early detection of skin cancer. There are a few studies in the literature that modelled the incidence and mortality of skin cancer [54-56]. The main drawback of these modelling studies is the difficulty in estimating tumor onset and progression. This could be addressed by forming a coalition of multiple modelling groups for skin cancer, like the Cancer Intervention and Surveillance Modelling Network (CISNET) group has done for other cancer sites [57].
Given the difficulties in access to high-quality care for early detection of skin cancer, there is considerable interest in developing algorithms and apps for skin cancer lesion assessment. Although mHealth apps have been criticized in the past due to their poor accuracy, the SkinVision app has a high accuracy to evaluate the risk of skin lesions for skin cancer. This was achieved due to improvements in the processing of images taken with the smartphone camera and a large risk-labeled image database from users, which was used to train a machine learning algorithm.
However, there are still many open questions regarding the usage of mHealth apps. National health authorities need to decide where to position these apps in the health care system (lay population, GPs, or dermatologists). Health effects of early and more accurate detection are difficult to estimate. There is currently no high-quality evidence on the health and cost benefits and harms of early detection of skin cancer, namely, on the trade-off between doctor visits and lives saved/advanced cases avoided. The reduction of the skin cancer burden on the health system and in the population could be substantial, as earlier detection of skin cancer could result in a lower average cost of treatment and a reduction in the number of doctor visits. However, further studies are needed to confirm this.
AU is affiliated with SkinVision, MW is an occasional advisor for SkinVision, and TN has received an unrestricted research grant from SkinVision.
Datasets used to train and test the SkinVision algorithm.
PDF File (Adobe PDF File), 432KB
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BCC: basal cell carcinoma
CISNET: Cancer Intervention and Surveillance Modelling Network
GP: general practitioner
mHealth: mobile health
MM: malignant melanoma
RCT: randomized control trial
SCC: squamous cell carcinoma
SVM: support vector machine
Edited by C Dias; submitted 14.01.19; peer-reviewed by E Hacker, J Robinson; comments to author 01.03.19; revised version received 20.05.19; accepted 19.06.19; published 11.07.19
©Tiago M de Carvalho, Eline Noels, Marlies Wakkee, Andreea Udrea, Tamar Nijsten. Originally published in JMIR Dermatology (http://derma.jmir.org), 11.07.2019.
de Carvalho TM, Noels E, Wakkee M, Udrea A, Nijsten T


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It's 2018 which – for smartphones – has definitely been the year of the notch. It's been the most popular approach by far to the problem of trying to get as much screen as possible on to the front of a smartphone. Undoubtedly, one of the biggest catalysts for that trend was 2017's iPhone X.
Fast forward to now, and the range has had an upgrade. What's more, this year Apple decided to make a big phone with an even bigger screen. When the manufacturer told us the iPhone X was a taste of the future, it wasn't kidding. iPhone XR, iPhone XS and the iPhone XS are all built on the foundation started by the iPhone X (big screen/no bezels) and expand.
The biggest of those, and the most expensive of those, is the iPhone XS Max. It's the ultimate in big premium flagship phones, but with prices up to GBP1,449, it's surely pushing the boundaries of what anyone is willing to spend on a handheld computer, no?
The easiest way to describe the looks and build of the iPhone XS Max is to say that it looks like a big iPhone X. Apple took last year's design and made it more water resistant, bigger and added a new colour option.
As an overall package the XS Max is similar in size to the previous generations of Plus models, except, Apple has made much better use of the space on the front. Apart from the distinguishable notch on the top, there's little to no bezel around the huge 6.5-inch display on the front.
Since the original iPhone X, it's become a look mimicked by almost every other manufacturer, although the iPhone's frame, particularly the bottom bezel, is impressively slim boosting the striking appearance of the display. Place it alongside the Samsung Galaxy Note 9, for example, and it's noticeably shorter despite having a display that's slightly larger.
In fact, it's only slightly taller than the OnePlus 6, which has a screen 0.2-inches smaller diagonally. Whatever opinions you have on Apple, the efficient use of space on the front here is brilliant. As with the X and newer XS, the frame is made from a polished stainless steel, a high end appearance and a sturdy feel in the hand.
Combined with the glass back, and the weight, it doesn't feel too dissimilar to the 8 Plus, although the polished metal frame does feel more grippy than the aluminium of previous iPhones. The polished steel on our gold review unit contrasts well with the black glass on the front, while the cream/peach glass back has a reflective gold sheen giving off a metallic finish similar to a lot of current smartphones. Although it doesn't have the eye-catching colour-changing properties of the likes of Huawei, Honor and HTC phones, still looks great.
The notch is something that's caused controversy ever since it first appeared on the iPhone X in 2017.
Since then, it's been copied by many competitors to expand the screen-to-body ratio, even Google with its most recent Pixel 3 XL. Until a more elegant solution is found, we feel it's a compromise worth making. In daily use, for the most part, it doesn't negatively impact the awesome viewing experience that is offered by the iPhone's 6.5-inch OLED display.
Of course, we'd rather it wasn't there, but with the addition of advanced and convenient Face ID recognition (which we'll go into later) it's justified.
We'd much rather that than a sliding mechanism like the Oppo Find X that negatively impacts the structural integrity of the device and makes it vulnerable to dust and water ingress. As for resolution, it's long been the case that Apple avoids media standard defaults. Rather than a QHD+ resolution, it has 1242 x 2688 pixels spread over the aforementioned 6.5-inch panel, giving a pixel density of 458 pixels per inch.
That puts it among some of the sharpest screens out there, putting it somewhere between the full HD+ panel of the OnePlus 6 and the Quad HD+ panel of the Note 9. That's to say, you won't be able to see any individual pixels here. Details are sharp, fine text is crisp and the whites and colours are really nicely balanced and tuned.
What's more, with it being capable of displaying both HDR 10 and Dolby Vision standards of HDR video, compatible movies do look really good on it. Compared to the likes of the iPhone 8 Plus – which had an LCD display – the dark blacks really stand out. It's inky, and gorgeous to look at.
While visually the screen is fantastic, the size would lend itself perhaps to be used as a miniature notepad of sorts. We'd love to have seen Apple incorporate support for the Apple Pencil, giving it that extra edge over its smaller sibling and giving the Note 9 some genuine like-for-like competition. You wouldn't want to use it for iPad Pro-level sketching and drawing, but for quickly annotating images, making short lists and sketching ideas, it could be a convenient addition.
Apple made a big deal of its new A12 Bionic processor, and for good reason.
It's among the first to make use of the 7nm process, making it possible to have much more power coming from a much smaller chip that's also more efficient.
It's something of an unsung hero, because – along with the under the hood changes made in iOS 12 – it ensures the phone runs smoothly and quickly without any issue. And should hopefully mean a phone that stays that way for years to come. Perhaps that way you can also justify the initial cost of the handset.
It will stay immensely useable for a considerable amount of time, without suffering from the post 12 month lag you sometimes find with older devices, particularly when new software is released. Gaming on the iPhone XS Max is as smooth and stable as you'd hope. Games load quickly and characters on screen respond to input and touchscreen gestures without any hesitation, which is also partly down to the 120Hz touch sensor response rate.
For smaller iPhones from the past few years (iPhone X not included) one of the biggest complaints from users is battery life. For the bigger devices, that's rarely an issue, and the same is true for the XS Max, which comfortably lasts a full day without even breaking a sweat. Even on the first day of use, with over 120 photos and videos shot on the camera, playing at least 30 minutes of games plus the usual drain that comes from setting up a phone, we still managed to get to bed with around 39 per cent of it left over.
On a regular, moderate day with still plenty of time (around 2 hours) using Bluetooth to listen to music and a Garmin fitness watch connected, we ended the day with around 50 per cent left over. For the heavy user, we suspect even the most demanding of days won't drain this battery. For moderate users, a day and a half isn't unreasonable.
Despite the fact we could probably have pushed it into a second day, we just fell back into the routine of resting on its wireless charging base and letting it top up over night while we slept.
One thing iPhone is missing here – compared to many others – is a fast charging wired charger in the box. Despite its size and capacious battery, the iPhone XS Max ships with the same 5W wall adapter and Lightning cable it's always shipped with. That means, unless you use a MacBook's more powerful power adapter or buy an upgrade, you're going to spend a long time waiting for it to go from zero to full.
Compare that to the likes of OnePlus' fast charging technology – which can get a battery enough juice to last a full day from just 30 minutes plugged in – and it's worlds apart. Moving from an iPhone 8 Plus, or older model, you may be worried about losing your fingerprint sensor, but with Face ID, we actually found unlocking our phone just as convenient and speedy, if not more so. With the lift-to-wake feature switched on, all you need to do is lift up your phone while looking at it, and the phone is normally unlocked before you even touch the screen to action the swipe-up-to-unlock gesture.
It's super easy, and hasn't failed once in our time with the device.
iOS 12 was launched to be part of Apple's plan to get your old iPhones running for longer, speeding up your phone an lightening the load on your old processor. What that means in turn is that brand new phones run really smoothly, but there are some new UI elements and features, particular for those using iPhone X. Having multiple notifications truncate into a single expandable pile is really useful, particularly if you have a busy social media presence, or receive tonnes of notifications.
Tap to expand or swipe across to perform bulk actions (like clearing them all from your lock screen). For iPhone X, XS or upcoming XR series devices there's the ability to make use of the fun, playful animated emoji that react to your facial expressions. With iOS 12 you can also create your own emoji character called a Memoji, that you can either make look like you or whatever you want, choosing face shape, skin colour, eye colour, eye wear, facial hair and a lot more.
While it's not as easy as Samsung's method that scans your face and generates an emoji character for you, it does mean you can make it look vaguely like you, unlike Samsung's, which inevitably doesn't.
There's plenty more here too, like the return of the landscape view in some of the stock apps, similar to the Plus model phones' of previous years. Turn Messages to its side and you see an inbox on the left, and conversation on the right. In Mail, the use of space is a little less practical, with the inbox sliding over the left side of the screen covering half of whatever email you're trying to read.
As for Siri shortcuts, that's something that's still in beta mode. Or at least, the full app is. Once officially out, you'll be able to craft your own recipes for Siri to action.
For now, that's fairly limited to some stock app actions, like navigation in Apple Maps, or sending pre-written messages to a specific contact.
Apple's latest rear dual camera system is made up of two 12-megapixel sensors, one offering a wide angle view, and the other a telephoto zoom view, with both cameras optically stabilised. But that only tells part of the story. As with the performance, there's a sense here that the new custom 7nm processor inside the phone is unlocking much more and extracting great quality images from the dual sensors.
Part of this is a feature called Smart HDR that's turned on by default. What it means is that even when you have some fairly intense backlighting from the sun, you can still see details in the shadows and dark areas, and colours are still vibrant and attractive. Even more impressive is that when you take the photo, there's nothing to indicate that it's working on the processing.
You snap the picture, and it's saved as simply and quickly as if you're taking any photo.
From a user perspective, it's similar to the way Google's Pixel phones have done things over the past couple of years. Snap a picture, and the processing power takes care of balancing out all the colours, highlights and shadows to make as good an image as possible without you having to do any of the heavy lifting. In Apple's case, the camera takes multiple pictures in the the split second before and after you press the shutter, including a long exposure, simultaneously taken with the super-quick burst to give the ISP all the information it needs to balance out the pic.
The end result is generally impressive, looking detailed, colourful and as detailed as you'd hope in challenging conditions, without over-boosting contrast too much. Even with faces in complete shadow with a backlighting sun, the camera still manages to pull out some detail from the face. One other headline feature is the new ability to adjust the depth effect during portrait mode shoot.
It works in a similar way to Huawei/Honor's aperture mode in that you take your photo in Portrait mode, and you can slide over an on-screen control to change the aperture, adjusting the depth of field and increasing or decreasing the blur. While cranking up the blur often has the unwanted effect of making the blur seem far too unnatural, decreasing it has the opposite impact. In fact, that's probably this feature's strength: it gives you the ability to make your portrait mode photos – complete with artificial lighting effects – less unnatural.
As you'd expect, there's still the odd occasion when edges of people's hair gets confused with the background, resulting in either the hair becoming blurred or a snippet of the background being too sharp.
Since the inception of the iPhone, part of its joy (and frustration for pro photographers) is the fact that it takes care of your photos for you. Just hit the shutter button and it'll make the picture look good, and that's the same with the latest generation of iPhones. You don't get granular controls, the iPhone takes care of all the heavy lifting for you.
As an overall experience, the iPhone XS Max is hard to fault.
It's fluid, fast and effortless, and is a premium package that looks and feels great in the hand. It may be too big for some, or too expensive for others, but there's always the iPhone XS for those who think the former, and the upcoming iPhone XR for those who think the latter. If you want the biggest, baddest iPhone in town, the XS Max is the one for you.
For years Samsung has provided the most consistent competition to Apple in the flagship smartphone game, and with the Galaxy Note 9 it has one of the best big-screened devices out there. The display might not have the same visual impact or skinny bezels, but it's fantastic, and it has the S-Pen, which for many is a great additional tool.
If you want one of the most eye-catching phones to have ever been launched with one of the most impressive cameras we've seen, the P20 Pro is the phone for you. The rainbow-coloured Twilight finish is remarkable and the AI stabilisation used in handheld night time shots really has to be seen to be believed.
You would think that it would be an exaggeration to say "you could buy two [X phones] for the price of an iPhone XS Max", but in the case of the OnePlus 6, it's literally true. With its starting price of GBP469, you'd have plenty of change left as well. It might not offer the surgical steel frame, tight-ecosystem or market-leading customer service, but its 6.3-inch display is bright and colourful and the fast charging is superb.
It also happens to be speedy and smooth with all-day battery.


is the educational home of Spiritual Teacher, Meditation Master and EFT Expert GP Walsh. It is the official site for registering for classes by GP. Join the thousands who have benefited from his teaching and healing.
All purchases from MasterHEART Institute can be returned within 60 days from the date of purchase for a full, 100% refund.


I don't know if Ako Ay Pilipino (…May dugong maharlika…) was deliberately intended as a counter-anthem to Bayan Ko, but it sure felt that way, and was that the reason why Cory refused to name Levi Celerio a National Artist?
Reyes said that it was supposedly a counter-anthem for the EDSA song, "Bayan Ko." The abogado said that it really felt that way and could be the reason why Celerio was overlooked as a national artist.
Pasabog talaga tong si Sir!


text:
The Hassel is a river of Saxony-Anhalt, in the East Harz Mountains in Germany. It flows through several municipalities including Stiege and the town of Hasselfelde. Its source is located not far from Stiege. After about  it flows into the Hassel Auxiliary Dam and later into the Rappbode Reservoir.

See also
List of rivers of Saxony-Anhalt

Rivers of Saxony-Anhalt
Rivers of the Harz
Rivers of Germany

redpajama_set_name: RedPajamaWikipedia


text:
LatestNewsTrending
Andrew Tate's Hustler's University affiliate program closed after his ban on Meta
By Esportsgen Desk August 22, 2022 0
Andrew Tate's Hustler's University affiliate program closed after his ban on Meta – One of the most controversial Internet personalities of recent times is social media influencer, Andrew Tate. Following his bans on multiple social media platforms including Facebook, Twitter, and Instagram, his controversial Hustler's University affiliate program has been shut down.
Members of this university pay a monthly fee of $50 to obtain lessons on subjects including cryptocurrency, e-commerce, and stock trading through an online educational program that lacks institutional recognition. One of the key factors that contributed to Tate's success is this marketing scheme program, which persuaded users to enlist new ones and post videos of Tate in exchange for a commission.
 Andrew Tate's Hustler's University Program: An investigative report by the Guardian informed that Hustler's University has been closed the day after his ban, claiming it has 'no future. In addition, the membership of the academy has drastically decreased, sitting at just 109k students today compared to the 127k members mark just two weeks ago.
This all hints toward the decreasing popularity of Tate after facing huge criticism for his 'misogynistic' and 'sexist' comments. And this reduction in popularity might be a result of TikTok's recent stepped-up enforcement of its policies, which led to the removal of countless Andrew Tate videos and accounts.
Andrew Tate's negative popularity shot up after he was banned from Meta owed platforms, stating that he had violated their guidelines regarding "dangerous organizations and individuals." Tate has previously been banned from Twitter for writing that sexual assault victims "should bear some responsibility." Many streamers including Asmongold, Hasan Abi, and others criticized him for the views and opinions that he held regarding women.
Tate's Hustler's University had an affiliated marketing program, which was essentially a pyramid scheme, which is now closed. However, it is still providing its classes (at $50 per month) as usual, but the financial incentive for students for bringing new students has been removed.
Read More- PewDiePie shares his thought on MrBeast overtaking him on YouTube
Andrew Tate's Hustler's University Program
Hustler's University Program
Pokemon Championship Series 2023 will be taking place in Yokohama, Japan
PUBG Mobile 2.2 Halloween Update: Here Is All you Need To Know
More in Latest
Legend X BGMI ID: Check Out Laksh Gupta's BGMI ID, F/D Ratio, YouTube Earnings, and more
PUBG Mobile 2.5 Update Leaks: New Creative Mode Map leaked for the upcoming PUBG Mobile 2.5 Update download
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ITPS (Cyprus) Corporate Services Ltd.
THE ITPS GROUP is an independent provider of international tax planning and structure services licensed by the Dutch Central Bank.
It provides these services in the Netherlands, Belgium, Belize, the British Virgin Islands, Curacao, Cyprus, Hong Kong, Luxembourg and Malta.
Design and development by PersC Communicatie B.V.


Ruach Designs are a kitchen design company based in Westerham. Ruach (pronounced /roo'och/ to rhyme with 'clock') is an ancient Hebrew word meaning 'spirit'. As the heart of the home, the kitchen is a deeply personal space that should reflect your own unique spirit.
At Ruach Kitchens our aim is to understand your aspirations and deliver a luxury bespoke kitchen that surpasses your dreams.
Located on Westerham High Street, our story started in 1993 in the spare room of Managing Director Ian Penman's home. Ian built the business up from scratch and now employs his son and daughter in the firm. We continue to take pride in the family feel of our company and offer a friendly, personal touch that focuses on our clients' individual needs. From the initial kitchen design to the installation and aftercare, we will help you find your ideal kitchen at a price that's affordable to you.
Whether it's an award-winning contemporary Mobalpa kitchen or our stunning bespoke, handmade range we can offer a large choice for all budgets. We also supply market-leading appliances including Miele, Siemens, Neff and AEG ensuring practical and fully functional kitchens.
Our approach is design-led, which means we're up to date with the latest kitchen trends, so you don't have to be. With a free design and consultation service, conducted by a team of qualified interior designers, you'll get the best advice on colours, textures and kitchen styles.
We have our own team of installers and only work with trusted partners to deliver a quality kitchen design service from start to finish. You also have the peace of mind of knowing that with Ruach Kitchens everything, including appliances, is fully guaranteed.
If you are looking for some kitchen inspiration we also have our own kitchen showroom located in Westerham. Be sure to pay us a visit to see our range of luxury kitchens for yourself.


text:
Judy, Judy, Judy
Yesterday, I saw the wonderful new Renee Zellweger movie about the last days of the incomparable Judy Garland. Goober, one of the later additions to the Andy Griffith Show, that I mentioned in a blog recently, on the show would try to entertain people with his celebrity imitations which consisted of only one and sounded nothing like Cary Grant. Goober would just say Judy, Judy, Judy real fast. But it always got a laugh.
Judy, Judy, Judy we adored you and we miss you belting them out for us. If we had only known how hungry your heart was for love we would have loved you harder, bigger, better. We hope you found that blue bird you were always looking for in your next adventure Over The Rainbow.
https://drive.google.com/file/d/0B5UGucaz8iwNRkd2OUFtV0VfUk9xOUFqRG9vSjQ4V3o4TlU0/view?usp=drivesdk
What follows is my glowing review of the movie Judy with the hope that each one reading this will see it if you have not already. I plan to see it again with I am sure tears in my eyes again:
Renee Zellweger's haunting performance of Judy Garland's last big singing performance in London was especially so to me. For every year since I was a boy I looked forward to seeing Judy visit Oz and sing Over The Rainbow. I knew all of her performances with Mickey Rooney as Andy Hardy as well. And since my mother was a life-long adoring fan of Clark Gable, Judy's singing tribute to "Dear Mister Gable" was a special favorite in our house.
Some long-time Judy fans might be disturbed at the mess her life was toward the end but perhaps that it was not always is just the illusion of the silver screen. Judy's gift, her marvelous voice, was packaged and used by the studio system to make large profits for the owners as was the look and voice of Marilyn Monroe and so many lovely luminaries trying to fill the great emptiness they perceived inside them. But that lie was never true of Judy nor of any of us really for as my lost but never really lost token reads so do all our hearts if we but look closely there to find the true answer to our eternal identity:
My first young wife, Barbara, was a smoker. Twenty years ago the end of this month, she, like Judy passed off this life's stage at forty seven of a stroke. Like Judy also she left two young children in the care of their father. She was my wife ever so briefly when we were quite young. Our marriage was over before it ever really began just like Judy's last marriage.
Barbara, too, had a lovely voice but never the fan base Judy had. She smoked and drank like Judy trying to fill a misperceived emptiness inside her and wore herself out early doing so. Or at least that is how it seems to me. Perhaps it was always their plan Judy's, Barbara's, Marilyn's, and my Emily's but to have shorter performances than some of the rest of us. My, how they shined.
One of Judy's last lines in the movie is, "You're gonna miss me." She was so right. A thing of beauty is a joy forever. I shared my cover of Judy's Over The Rainbow. Hers as a young girl in Kansas dreaming of heaven or Oz is incomparable. I do so enjoy Israel Kamakawiwo'ole's shortened ukulele version and Josh Groban's longer version with the extra verse but no one could ever sing it like Judy. And, yes, darling, we miss you and all your lovely singing sisters who have already left the stage. See you later, dear ones.
I do hope you will all spend a couple of hours with Judy. It is not your typical Hollywood princess tale but then neither was her life nor are any really if you look beyond the glamour and glitter. On the screen at the end of the movie flash several important pieces of information, first that six months after her last singing performance in London that Judy died at forty seven. But, even more importantly a quote that sizes up Judy's life and all of our lives really, a quote from one if not her most beloved movie.
You are so loved.
By David White3 Comments
3 thoughts on "Judy, Judy, Judy"
Such a sad end, I always had the view that she'd been so manipulated.
David White says:
Yes, I agree and Marilyn Monroe too and so many others, ourselves too often too as well, but only because I think we forget Who and Whose we are.
Absolutely David.
← Every Now And Again
What You See In Me →


text:
Anni
39 – anno del I secolo d.C.
39 a.C. – anno del I secolo a.C.

Matematica
39 – numero naturale


text:
A New Gear
If last week's snow made you think the two-wheeling season is over, think again. Tuesday, Nov. 29, is the official launch date of Traverse City's brand-new elite mountain bike racing team, the Great Lakes Off Road Initiative (GLORI). Over the past decade, northern Michigan has become a hub for talented cyclists, and this new team will offer young adult racers the chance to train for national and international competitions in 2023 and beyond. GLORI's team manager, Justin Morris, is a former pro cyclist himself and says he is excited to "to enable the dreams of the many young local talented cyclists" with the help of several local businesses. GLORI will also "give the riders opportunity to pursue world ranking points which are a crucial step in obtaining selection for national teams and future professional teams." The new team is rolling out with a soft launch on Nov. 29 at 6pm at Brick Wheels in Traverse City. Learn more at gloriracing.com.
Photo by Rob Meendering


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| 145401 - || || 4 de juliol de 2005 || Palomar || NEAT
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| 145402 - || || 7 de juliol de 2005 || Kitt Peak || Spacewatch
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| 145403 - || || 8 de juliol de 2005 || Kitt Peak || Spacewatch
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| 145404 - || || 10 de juliol de 2005 || Reedy Creek || J. Broughton
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| 145405 - || || 13 de juliol de 2005 || Reedy Creek || J. Broughton
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| 145406 - || || 1 de juliol de 2005 || Kitt Peak || Spacewatch
|-
| 145407 - || || 1 de juliol de 2005 || Kitt Peak || Spacewatch
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| 145408 - || || 2 de juliol de 2005 || Kitt Peak || Spacewatch
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| 145409 - || || 3 de juliol de 2005 || Palomar || NEAT
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| 145410 - || || 4 de juliol de 2005 || Socorro || LINEAR
|-
| 145411 - || || 10 de juliol de 2005 || Kitt Peak || Spacewatch
|-
| 145412 - || 2005 OO || 16 de juliol de 2005 || Kitt Peak || Spacewatch
|-
| 145413 - || || 26 de juliol de 2005 || Palomar || NEAT
|-
| 145414 - || || 29 de juliol de 2005 || Palomar || NEAT
|-
| 145415 - || || 27 de juliol de 2005 || Reedy Creek || J. Broughton
|-
| 145416 - || || 30 de juliol de 2005 || Palomar || NEAT
|-
| 145417 - || || 2 d'agost de 2005 || Socorro || LINEAR
|-
| 145418 - || || 4 d'agost de 2005 || Palomar || NEAT
|-
| 145419 - || || 4 d'agost de 2005 || Palomar || NEAT
|-
| 145420 - || || 6 d'agost de 2005 || Siding Spring || SSS
|-
| 145421 - || || 2 d'agost de 2005 || Socorro || LINEAR
|-
| 145422 - || || 7 d'agost de 2005 || Haleakala || NEAT
|-
| 145423 - || || 24 d'agost de 2005 || Palomar || NEAT
|-
| 145424 - || || 25 d'agost de 2005 || Palomar || NEAT
|-
| 145425 - || || 26 d'agost de 2005 || Palomar || NEAT
|-
| 145426 - || || 26 d'agost de 2005 || Palomar || NEAT
|-
| 145427 - || || 27 d'agost de 2005 || Anderson Mesa || LONEOS
|-
| 145428 - || || 26 d'agost de 2005 || Palomar || NEAT
|-
| 145429 - || || 27 d'agost de 2005 || Anderson Mesa || LONEOS
|-
| 145430 - || || 29 d'agost de 2005 || Anderson Mesa || LONEOS
|-
| 145431 - || || 25 d'agost de 2005 || Palomar || NEAT
|-
| 145432 - || || 25 d'agost de 2005 || Palomar || NEAT
|-
| 145433 - || || 27 d'agost de 2005 || Anderson Mesa || LONEOS
|-
| 145434 - || || 30 d'agost de 2005 || Socorro || LINEAR
|-
| 145435 - || || 27 d'agost de 2005 || Bergisch Gladbach || W. Bickel
|-
| 145436 - || || 25 d'agost de 2005 || Palomar || NEAT
|-
| 145437 - || || 26 d'agost de 2005 || Haleakala || NEAT
|-
| 145438 - || || 27 d'agost de 2005 || Palomar || NEAT
|-
| 145439 - || || 27 d'agost de 2005 || Palomar || NEAT
|-
| 145440 - || || 26 d'agost de 2005 || Campo Imperatore || CINEOS
|-
| 145441 - || || 26 d'agost de 2005 || Palomar || NEAT
|-
| 145442 - || || 28 d'agost de 2005 || Anderson Mesa || LONEOS
|-
| 145443 - || || 31 d'agost de 2005 || Palomar || NEAT
|-
| 145444 - || || 31 d'agost de 2005 || Kitt Peak || Spacewatch
|-
| 145445 - || 2005 RS || 2 de setembre de 2005 | Saint-Véran || Saint-Véran
|-
| 145446 - || || 8 de setembre de 2005 || Socorro || LINEAR
|-
| 145447 - || || 1 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145448 - || || 6 de setembre de 2005 || Socorro || LINEAR
|-
| 145449 - || || 10 de setembre de 2005 || Anderson Mesa || LONEOS
|-
| 145450 - || || 13 de setembre de 2005 || Črni Vrh || Črni Vrh
|-
| 145451 - || || 9 de setembre de 2005 || Apache Point || A. C. Becker, A. W. Puckett, J. Kubica
|-
| 145452 - || || 10 de setembre de 2005 || Apache Point || A. C. Becker, A. W. Puckett, J. Kubica
|-
| 145453 - || || 9 de setembre de 2005 || Apache Point || A. C. Becker, A. W. Puckett, J. Kubica
|-
| 145454 - || || 23 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145455 - || || 23 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145456 - || || 24 de setembre de 2005 || Vicques || M. Ory
|-
| 145457 - || || 23 de setembre de 2005 || Anderson Mesa || LONEOS
|-
| 145458 - || || 23 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145459 - || || 25 de setembre de 2005 || Catalina || CSS
|-
| 145460 - || || 23 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145461 - || || 27 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145462 - || || 27 de setembre de 2005 || Socorro || LINEAR
|-
| 145463 - || || 26 de setembre de 2005 || Palomar || NEAT
|-
| 145464 - || || 27 de setembre de 2005 || Socorro || LINEAR
|-
| 145465 - || || 25 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145466 - || || 28 de setembre de 2005 || Palomar || NEAT
|-
| 145467 - || || 28 de setembre de 2005 || Palomar || NEAT
|-
| 145468 - || || 29 de setembre de 2005 || Kitt Peak || Spacewatch
|-
| 145469 - || || 27 de setembre de 2005 || Palomar || NEAT
|-
| 145470 - || || 27 de setembre de 2005 || Palomar || NEAT
|-
| 145471 - || || 29 de setembre de 2005 || Mount Lemmon || Mount Lemmon Survey
|-
| 145472 - || || 22 de setembre de 2005 || Palomar || NEAT
|-
| 145473 - || || 23 de setembre de 2005 || Palomar || NEAT
|-
| 145474 - || || 27 de setembre de 2005 || Apache Point || A. C. Becker, A. W. Puckett, J. Kubica
|-
| 145475 Rehoboth || || 12 d'octubre de 2005 || Calvin-Rehoboth || L. A. Molnar
|-
| 145476 - || || 2 d'octubre de 2005 || Palomar || NEAT
|-
| 145477 - || || 5 d'octubre de 2005 || Catalina || CSS
|-
| 145478 - || || 3 d'octubre de 2005 || Palomar || NEAT
|-
| 145479 - || || 7 d'octubre de 2005 || Kitt Peak || Spacewatch
|-
| 145480 - || || 11 d'octubre de 2005 || Apache Point || A. C. Becker, A. W. Puckett, J. Kubica
|-
| 145481 - || || 23 d'octubre de 2005 || Catalina || CSS
|-
| 145482 - || || 24 d'octubre de 2005 || Palomar || NEAT
|-
| 145483 - || || 25 d'octubre de 2005 || Catalina || CSS
|-
| 145484 - || || 23 d'octubre de 2005 || Palomar || NEAT
|-
| 145485 - || || 31 d'octubre de 2005 || Anderson Mesa || LONEOS
|-
| 145486 - || || 28 d'octubre de 2005 || Kitt Peak || Spacewatch
|-
| 145487 - || || 29 d'octubre de 2005 || Catalina || CSS
|-
| 145488 - || || 4 de novembre de 2005 || Piszkéstető || K. Sárneczky
|-
| 145489 - || || 4 de novembre de 2005 || Catalina || CSS
|-
| 145490 - || || 1 de novembre de 2005 || Anderson Mesa || LONEOS
|-
| 145491 - || || 6 de novembre de 2005 || Mount Lemmon || Mount Lemmon Survey
|-
| 145492 - || || 21 de novembre de 2005 || Anderson Mesa || LONEOS
|-
| 145493 - || || 21 de novembre de 2005 || Catalina || CSS
|-
| 145494 - || || 22 de novembre de 2005 || Kitt Peak || Spacewatch
|-
| 145495 - || || 22 de novembre de 2005 || Kitt Peak || Spacewatch
|-
| 145496 - || || 27 de novembre de 2005 || Socorro || LINEAR
|-
| 145497 - || || 5 de desembre de 2005 || Kitt Peak || Spacewatch
|-
| 145498 - || || 30 de desembre de 2005 || Kitt Peak || Spacewatch
|-
| 145499 - || || 21 de gener de 2006 || Kitt Peak || Spacewatch
|-
| 145500 - || || 24 de gener de 2006 || Socorro || LINEAR
|-|}

5


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Much of the glass, metal, paper and plastic we use daily heads straight to the landfill, usually after a single use, yet these items have not reached the end of their life cycle. Keep Genesee County Beautiful believes that conserving resources is important and recycling is one way in which to do so. KGCB has become the "go-to" resource for recycling education in Genesee County and provides information on what can and cannot be recycled in curbside programs and how to prepare it.
Click here for a list of year-round locations that accept hazardous waste, electronic waste and items not permitted in curbside trash or as part of recycling collection.
City of Flint Residents – for information regarding curbside recycling services and guidelines from Republic Services, click here.
Need some help figuring out how to prepare your recycling? Click here for Recycling Bin Basics.


Asst. Prof. Özge Sağlam, Lecturer, Department of Mechanical Engineering, Faculty of Engineering, Izmir University of Economics, will be researching the next generation display technology. Sağlam's TUBITAK funded project will manufacture capacitors for more efficient and durable smart phone and television screens and wearable technologies.


text:
Regional Medical Campus Educators Recognized at George Paff Teaching Awards Ceremony
Peter Laird
Miller School's Medical Scholars Fellowship Program Promotes Diversity in Medicine
SURF's Up This Summer at the Miller School
Annual Exercises Test Clinical Skills of Third-Year Medical Students
Stay up to date with the latest medical news and research at UHealth.
Julia Belkowitz Lichtenstein, M.D., M.P.H., assistant regional dean for student affairs (left), with Bobby Kumar, M.D., recipient of the Distinguished Pediatrics Educator Award, and Nicole Molinos, M.D., recipient of the Distinguished Pediatrics Resident Educator Award
A select group of faculty, residents, and staff from the University of Miami Miller School of Medicine Regional Medical Campus (RMC) were recognized at the 2019 RMC George Paff Teaching Awards Ceremony held May 16 at John F. Kennedy Medical Center in Atlantis.
The RMC George Paff Teaching Awards Ceremony is an annual celebration organized by current third- and fourth-year medical students on the regional campus who nominate clinical educators in a variety of categories.
Kimberly and Kelly Withum accept the Distinguished Public Health Educator Award, presented by Waseem Nosair, M.D./M.P.H. student, in honor of their father, the late David Withum, Dr.PH, M.P.H.
The late David Withum, Dr.P.H., M.P.H., who died in January, was honored with the Distinguished Public Health Educator Award. Before becoming a voluntary assistant professor in epidemiology and public health at the Miller School, Dr. Withum served in multiple advisory roles in government and as senior epidemiologist at the Centers for Disease Control and Prevention. His family was present at the ceremony to receive the honor in his stead.
The James J. Byrnes Award was presented in memory of Dr. James Byrnes, an "outstanding physician and educator" who died in 2013 and who was the first Family Medicine Clerkship Director on the regional campus, according to Daniel Lichtstein, M.D., M.A.C.P., who has served as the Miller School's regional dean for medical education since 2010.
"The RMC George Paff Teaching Awards are an opportunity for faculty, preceptors, and residents to be recognized by the students who work and learn by their sides," said Dr. Lichtstein, who received the Byrnes award.
Dudley Brown, Jr., M.D., accepts the Distinguished Obstetrics and Gynecology Award from M.D./M.P.H. student Anise Crane
During the ceremony, students praised their teachers and mentors through personal anecdotes. As an added feature of this year's award presentations, students also shared stories about the lessons they've learned from patients they've worked with over the past year.
The following regional campus faculty, residents and staff members were recognized at the ceremony:
Distinguished Staff Member: Erica Simms
Distinguished Community Partner: Tooth Fairy Dental Program
James J. Byrnes Award: Daniel Lichtstein, M.D., M.A.C.P.
Distinguished Clerkship Director: Sarah Bland, M.D.
Distinguished Emergency Medicine Educator: Nishma Sachedina, M.D.
Distinguished Neurology Educator: Teresita Casanova, M.D.
Distinguished Public Health Educator: David Withum, Dr.P.H., M.P.H.
Distinguished Family Medicine Educator: Daniela Capota, M.D., D.P.M.
Distinguished Integrated Medicine Educator: Jason Yarsley, D.O.
Distinguished Integrated Medicine Resident Educator: Stan Ivanov, M.D., M.P.H.
Distinguished Integrated Surgery Educator: William Caskey, M.D.
Distinguished Integrated Surgery Resident Educator: Marcus Eby, M.D.
Distinguished OB/GYN Educator: Dudley Brown, Jr., M.D., M.B.A.
Distinguished Pediatrics Educator: Bobby Kumar, M.D.
Distinguished Pediatrics Resident Educator: Nicole Molinos, M.D.
Distinguished Psychiatry Educator: Irl Extein, M.D.
Student Stowe McMurry presents the James J. Byrnes Award to Daniel Lichtstein, M.D., M.A.C.P., regional dean for the University of Miami Miller School of Medicine
Tags: Daniel Lichtstein, David Withum, James J. Byrnes, RMC George Paff Teaching Awards


text:
Loud and Clear at 103.5fm
By Matt Bingay • Jan 8, 2021
An early morning shot of the Castle Rock tower with the damaged WMRY antenna about half-way up.
Matt Bingay
Climbing gear and safety harnesses for the Shenandoah Tower crew.
Two climbers from Shenandoah Tower head up to start dismantling the damaged antenna.
A crewman securely attached to the outside of the tower prepairs to release the old antenna.
A close up of the damaged antenna, showing a severe bend at the flange.
Unboxing the new WMRY 103.5fm antenna, built by ERI.
The new WMRY antenna, installed and working at full power.
Bill Fawcett
At a time when most organizations were preparing to manage the holidays, WMRA scrambled to address an almost catastrophic failure on the main signal serving Charlottesville and surrounding areas.
On December 16, 2020, a winter storm blanketed the area with snow and ice. Not an unusual event in and of itself, but that's where this tale begins. Sometime in the following 36 hours, as melting began, a large chunk of ice fell from an upper structure on our Castle Rock tower and struck the WMRY 103.5fm antenna with enough force to bend it out of shape.
We didn't know it at that exact moment, but this caused the end of a 25-year run for that antenna and an unexpected 10K expense for WMRA.
Monday, December 21, 2020, our engineers were able to investigate on site, and after a few attempts to bend the antenna back into shape, it became clear that this was not possible. Later that afternoon, we made the decision to replace the antenna as soon as possible.
One side note, broadcast antennas are custom made to very specific specs which allow them to produce the necessary FCC licensed contour patterns at the frequency required. A new antenna would not be something we could buy off the shelf.
Tuesday, Dec 22, 2020, we contacted ERI Inc, the company that built the original antenna which was installed in 1996. After explaining the situation and our dire need, ERI committed to immediate construction just days before the Christmas holiday. Amazingly, they were able to ship the new antenna at the end of the next day, Wednesday, Dec 23. This year's holiday shipping season being what it was, we received the new antenna January 2, 2021.
The next step was to schedule the tower crew needed to replace the failing antenna and Shenandoah Tower Service came through.
On Thursday, January 7, 2021, we met a crew from Shenandoah Tower at our Crozet transmitter site at 9am and began to set the stage for a full antenna replacement. Luckily it was a gorgeous January day, no clouds, little wind, and not bitterly cold. A team of three managed the tower work, with two on the tower and one on the ground. At about 10:30am, the climbers loaded up the necessary tools and safety devices, which amounted to about 20lbs each, and headed up the tower.
The antenna is located 80 feet up the tower and the crew climbs a ladder in the center of the tower. Once each crew member was secure in place, every piece of the failing antenna was carefully removed and lowered by rope. A video of the old antenna coming down is available here.
Then after a brief break, the new antenna was hoisted up and an assembled. It was careful, deliberate work and thankfully, there were no major issues. We resumed full power transmission at 4:30pm that day.
It's not every day that you replace a broadcast antenna and with some luck, this new antenna will serve central Virginia for another quarter century!
WMRA Feature


Want to show your SLA spirit? We have apparel so that you can show your pride! We have a variety of styles available as shown below. Simply print the order form and turn it into the school office. Orders will be placed each fall and spring.