***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: This study assesses the performance of public-domain automated method ologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer 's disease ( AD ) and mild cognitive impairment ( MCI ) . Structural MR images of 54 age- and gender-matched healthy elderly individuals , subjects with probable AD , and subjects with MCI were collected at the University of Pittsburgh Alzheimer 's Disease Research Center . Hippocampi in subject images were automatically segmented by using AIR , SPM , FLIRT , and the fully deformable method of Chen to align the images to the Harvard atlas , MNI atlas , and r and omly selected , manually labeled subject images ( " cohort atlases " ) . Mixed-effects statistical models analyzed the effects of side of the brain , disease state , registration method , choice of atlas , and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations . Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations . Side of the brain , presence of AD , choice of reference image , and manual tracing protocol were also significant factors contributing to automated segmentation performance . Fully automated techniques can be competitive with human raters on this difficult segmentation task , but a rigorous statistical analysis shows that a variety of method ological factors must be carefully considered to insure that automated methods perform well in practice . The use of fully deformable registration methods , cohort atlases , and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation OBJECTIVE To determine whether magnetic resonance imaging ( MRI ) white matter hyperintensities ( WMH ) , whole-brain atrophy , and cardiovascular risk factors predict the development of cognitive decline and dementia . DESIGN Subjects were recruited into this prospect i ve cohort study and followed for incident cognitive decline for mean ( SD ) 6.0 ( 4.1 ) years . Magnetic resonance imaging dual-echo sequences , obtained at baseline , were used to determine the volume of WMH and the brain parenchymal fraction ( BPF ) , the proportion of the intracranial cavity occupied by brain . White matter hyperintensity volume was analyzed as the percentage of intracranial volume ( WMHr ) ; " high WMH " was defined as a WMHr more than 1 SD above the mean . SETTING General community . PATIENTS Volunteer sample consisting of 67 subjects with normal cognition and 156 subjects with mild cognitive impairment ( MCI ) . MAIN OUTCOME MEASURES Time to diagnosis of MCI ( among those with normal cognition at baseline ) or time to diagnosis of dementia , either all-cause or probable Alzheimer disease ( AD ) ( among those with MCI at baseline ) . Cox proportional hazards models were used for multivariable analysis . RESULTS High WMH was a predictor of progression from normal to MCI ( adjusted hazard ratio [ HR ] , 3.30 ; 95 % confidence interval [ CI ] , 1.33 - 8.17 ; P= .01 ) but not conversion from MCI to all-cause dementia . Conversely , BPF did not predict progression from normal to MCI but did predict conversion to dementia ( adjusted HR , 1.10 for each 1 % decrease in BPF ; 95 % CI , 1.02 - 1.19 ; P= .02 ) . When conversion to AD dementia was considered as the outcome , BPF was likewise a predictor ( adjusted HR , 1.16 for each 1 % decrease in BPF ; 95 % CI , 1.08 - 1.24 ; P < .001 ) , but high WMH was not . Past tobacco smoking was associated with both progression from normal to MCI ( adjusted HR , 2.71 ; 95 % CI , 1.12 - 6.55 ; P= .03 ) and conversion to all-cause dementia ( adjusted HR , 2.08 ; 95 % CI , 1.13 - 3.82 ; P= .02 ) , but not AD dementia . CONCLUSIONS These findings suggest that WMH are associated with the risk of progressing from normal to MCI . In persons whose cognitive abilities are already impaired , BPF predicts the conversion to dementia Computer-aided diagnosis of Alzheimer 's disease ( AD ) is a rapidly developing field of neuroimaging with strong potential to be used in practice . In this context , assessment of models ' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications . In this study , we investigated the efficacy of R and om Forest classifiers trained using different structural MRI measures , with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness . From The ADNI data base , 185 AD , and 225 healthy controls ( HC ) were r and omly split into training and testing data sets . 165 subjects with mild cognitive impairment ( MCI ) were distributed according to the month of conversion to dementia ( 4-year follow-up ) .  Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction . Using the result ing output , AD/HC classifiers were trained . Training included model tuning and performance assessment using out-of-bag estimation . Subsequently the classifiers were vali date d on the AD/HC test set and for the ability to predict MCI-to-AD conversion . Models ' between-cohort robustness was additionally assessed using the AddNeuroMed data set acquired with harmonized clinical and imaging protocol s. In the ADNI set , the best AD/HC sensitivity/specificity ( 88.6%/92.0 % — test set ) was achieved by combining cortical thickness and volumetric measures . The R and om Forest model result ed in significantly higher accuracy compared to the reference classifier ( linear Support Vector Machine ) . The models trained using parcelled and high-dimensional ( HD ) input demonstrated equivalent performance , but the former was more effective in terms of computation/memory and time costs . The sensitivity/specificity for detecting MCI-to-AD conversion ( but not AD/HC classification performance ) was further improved from 79.5%/75%–83.3%/81.3 % by a combination of morphometric measurements with ApoE-genotype and demographics ( age , sex , education ) . When applied to the independent AddNeuroMed cohort , the best ADNI models produced equivalent performance without substantial accuracy drop , suggesting good robustness sufficient for future clinical implementation Mild cognitive impairment ( MCI ) is a transitional stage between age-related cognitive decline and Alzheimer 's disease ( AD ) . For the effective treatment of AD , it would be important to identify MCI patients at high risk for conversion to AD . In this study , we present a novel magnetic resonance imaging (MRI)-based method for predicting the  MCI-to-AD conversion  from one to three years before the clinical diagnosis . First , we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm result ing in what we call the aggregate biomarker . The novel characteristics of the methods for learning the biomarkers are as follows : 1 ) We used a semi-supervised learning method ( low density separation ) for the construction of MRI biomarker as opposed to more typical supervised methods ; 2 ) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression ; 3 ) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies ; and 4 ) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a r and om forest classifier . We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer 's Disease Neuroimaging Initiative ( ADNI ) data base . With the ADNI data , the MRI biomarker achieved a 10-fold cross-vali date d area under the receiver operating characteristic curve ( AUC ) of 0.7661 in discriminating progressive MCI patients ( pMCI ) from stable MCI patients ( sMCI ) . Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-vali date d AUC score of 0.9020 in discriminating pMCI from sMCI . The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction . However , it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction Alzheimer 's disease ( AD ) is a chronic neurodegenerative disease of the central nervous system that has no cure and leads to death . One of the most prevalent tools for AD diagnosis is magnetic resonance imaging ( MRI ) , because of its capability to visualize brain anatomical structures . There is a variety of classification methods for automatic diagnosis of AD , such as support vector machines , genetic algorithms , Bayes classifiers , neural networks , r and om forests , etc . , but none of them provides robust information about the stage of the AD , they can just reveal the presence of disease . In this paper , a new approach for  classification of MRI images  using a fuzzy inference system is proposed . Two statistical moments ( mean and st and ard deviation ) of 116 anatomical regions of interests ( ROIs ) are used as input features for the classification system . A t-test feature selection method is used to identify the most discriminative ROIs . In order to evaluate the proposed system ,  MRI images from a data base consisting of 818 subjects ( 229 normal , 401 mild cognitive impairment and 188 AD subjects ) collected from the Alzheimer 's disease neuroimaging initiative ( ADNI ) is analyzed . The receiver operating characteristics ( ROC ) curve and the area under the curve ( AUC ) of the proposed fuzzy inference system fed by statistical input features are employed as the evaluation criteria with k-fold cross validation . The proposed system yields promising results in normal vs. AD classification with AUC of 0.99 on the training set and 0.8622±0.0033 on the testing set BACKGROUND  Mild cognitive impairment ( MCI ) is a transitional stage from normal aging to Alzheimer 's disease ( AD ) dementia . It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable . We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity ( HVI ) from structural MRI scans that may be useful for this purpose . OBJECTIVE To determine the utility of HVI in classification between stable and progressive MCI patients using the R and om Forest classification algorithm . METHODS We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI , cognitive tests , and genetic and demographic information to train a R and om Forest classifier in a sample of 164 MCI subjects categorized into two groups [ progressive ( n = 86 ) or stable ( n = 78 ) ] based on future conversion ( or lack thereof ) of their diagnosis to probable AD . RESULTS The overall accuracy of classification was estimated to be 82.3 % ( 86.0 % sensitivity , 78.2 % specificity ) . The accuracy in women ( 89.1 % ) was considerably higher than that in men ( 78.9 % ) . The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI . CONCLUSION The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy . There may be stronger indicators of imminent AD dementia in women with MCI as compared to men BACKGROUND Subjects with a mild cognitive impairment ( MCI ) have a memory impairment beyond that expected for age and education yet are not demented . These subjects are becoming the focus of many prediction studies and early intervention trials . OBJECTIVE To characterize clinical ly subjects with MCI  cross-sectionally and longitudinally . DESIGN A prospect i ve , longitudinal inception cohort . SETTING General community clinic . PARTICIPANTS A sample of 76 consecutively evaluated subjects with MCI were compared with 234 healthy control subjects and 106 patients with mild Alzheimer disease ( AD ) , all from a community setting as part of the Mayo Clinic Alzheimer 's Disease Center/Alzheimer 's Disease Patient Registry , Rochester , Minn. MAIN OUTCOME MEASURES The 3 groups of individuals were compared on demographic factors and measures of cognitive function including the  Mini-Mental State Examination , Wechsler Adult Intelligence Scale-Revised , Wechsler Memory Scale-Revised , Dementia Rating Scale , Free and Cued Selective Reminding Test , and Auditory Verbal Learning Test .  Clinical classifications of dementia and AD were determined according to the Diagnostic and Statistical Manual of Mental Disorders , Revised Third Edition and the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer 's Disease and Related Disorders Association criteria , respectively . RESULTS The primary distinction between control subjects and subjects with MCI was in the area of memory , while other cognitive functions were comparable . However , when the subjects with MCI were compared with the patients with very mild AD , memory performance was similar , but patients with AD were more impaired in other cognitive domains as well . Longitudinal performance demonstrated that the subjects with MCI declined at a rate greater than that of the controls but less rapidly than the patients with mild AD . CONCLUSIONS Patients who meet the criteria for MCI can be differentiated from healthy control subjects and those with very mild AD . They appear to constitute a clinical entity that can be characterized for 
    Output:
    
We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants , but the manual MRI technique was superior to automatic techniques in mixed ( mostly indirect ) comparisons .
The volume of hippocampus or medial temporal lobe , the most studied brain regions , showed low sensitivity and specificity and did not qualify structural MRI as a st and -alone add-on test for an early diagnosis of dementia due to Alzheimer 's disease in people with MCI .
This is consistent with international guidelines , which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer 's disease .