xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05963945</url>
    </required_header>
    <id_info>
      <org_study_id>00002</org_study_id>
      <nct_id>NCT05963945</nct_id>
    </id_info>
    <brief_title>Multi-Reader Retrospective Study Examining Carebot AI CXR 2.0.21-v2.01 Implementation in Everyday Radiology Clinical Practice</brief_title>
    <official_title>Multi-Reader Retrospective Study Examining Carebot AI CXR 2.0.21-v2.01 Implementation in Everyday Radiology Clinical Practice</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Carebot s.r.o.</agency>
        <agency_class>Industry</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Carebot s.r.o.</source>
    <oversight_info>
      <has_dmc>Yes</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot&#xD;
        AI CXR) in comparison to individual radiologists.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        In the period between October 18th, 2022, and November 17th, 2022, anonymized chest X-ray&#xD;
        images of patients were collected at the Radiodiagnostic Department of the Havířov Hospital,&#xD;
        p.o. The collection process involved utilizing the CloudPACS imaging and archiving system&#xD;
        provided by OR-CZ spol. s r.o.&#xD;
  &#xD;
        The collected X-ray images were subjected to the proposed DLAD (Carebot AI CXR) for analysis.&#xD;
        Subsequently, the DLAD's performance was compared with the standard clinical practice, where&#xD;
        radiologists assessed the CXR images in the simulated hospital setting with access to&#xD;
        standard viewing tools (e.g., pan, zoom, WW/WL) and were given an unlimited amount of time to&#xD;
        complete the review. Each radiologist determined the presence or absence of 7 indicated&#xD;
        radiological findings, including atelectasis (ATE), consolidation (CON), pleural effusion&#xD;
        (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and&#xD;
        pneumothorax (PNO).&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Completed</overall_status>
    <start_date type="Actual">October 18, 2022</start_date>
    <completion_date type="Actual">March 21, 2023</completion_date>
    <primary_completion_date type="Actual">November 17, 2022</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Performance test</measure>
      <time_frame>March 2023</time_frame>
      <description>The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot AI CXR) in comparison to individual radiologists. The performance test includes sensitivity and specificity, positive and negative likelihood ratio, and positive and negative predictive value. The aforementioned parameters are statistically compared using confidence intervals (CI) and p-Values. The comparison procedure consists of two steps: a global hypothesis test is conducted to determine whether there are significant differences between DLAD and radiologists. If the global hypothesis test yields a significant result, individual hypothesis tests are performed. Additionally, multiple comparison methods, such as McNemar with continuity correction for Se and Sp, Holm method for LRs, and weighted generalized score statistics for PVs, are applied to control the overall error rate. All tests are performed as two-tailed tests at the 5% significance level.</description>
    </primary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Actual">956</enrollment>
    <condition>Pneumothorax</condition>
    <condition>Pulmonary Nodule, Solitary</condition>
    <condition>Atelectasis</condition>
    <condition>Subcutaneous Emphysema</condition>
    <condition>Cardiomegaly</condition>
    <condition>Consolidation</condition>
    <condition>Pleural Effusion</condition>
    <arm_group>
      <arm_group_label>Retrospective collection for the period October 18th, 2022, and November 17th, 2022</arm_group_label>
      <description>A total of 1,073 chest X-rays were acquired within the specified period at the department. The data collection remained intact and unaffected throughout the testing phase, ensuring the integrity of the dataset. The collected sample accurately represents the prevalence of findings within the observed population. After excluding ineligible studies such as X-rays from patients under 18 years of age, lateral projection X-rays, and scans of insufficient quality, a total of 956 relevant CXRs were identified for further assessment.</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>Carebot AI CXR</intervention_name>
      <description>The proposed DLAD (Carebot AI CXR) is a deep learning-based medical device designed to assist radiologists in interpreting chest X-ray images acquired in anteroposterior (AP) or posteroanterior (PA) projection. By employing advanced deep learning algorithms, this solution enables automatic detection of abnormal findings by analyzing visual patterns associated with specific conditions. The targeted abnormalities include atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and pneumothorax (PNO). The DLAD functions as a prediction algorithm complemented by various application peripherals, such as web-based communication tools, DICOM file conversion capabilities, and storage and reporting libraries supporting both DICOM Structured Report and DICOM Presentation State formats.</description>
      <arm_group_label>Retrospective collection for the period October 18th, 2022, and November 17th, 2022</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Patient's Sex Female: 480 (50.21 %) Male: 474 (49.58 %) Unspecified: 2 (0.21 %)&#xD;
  &#xD;
          Patient's Age 18-30: 58 (6.07 %) 31-50: 163 (17.05 %) 51-70: 366 38.28 %) 70+: 369 (38.60&#xD;
          %)&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Hospital patients &gt; 18 years who were referred for chest radiography October 18th,&#xD;
               2022, and November 17th, 2022 at the Radiodiagnostic Department of the Havířov&#xD;
               Hospital, p.o.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients &lt; 18 years&#xD;
  &#xD;
            -  Chest X-ray images in lateral positions&#xD;
  &#xD;
            -  Duplicated chest X-ray images&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
    </eligibility>
    <location>
      <facility>
        <name>Nemocnice Havířov, p. o.</name>
        <address>
          <city>Havířov</city>
          <zip>73601</zip>
          <country>Czechia</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Czechia</country>
    </location_countries>
    <results_reference>
      <citation>KVAK, Daniel, Anna CHROMCOVÁ, Petra OVESNÁ, Jakub DANDÁR, Marek BIROŠ, Robert HRUBÝ, Daniel DUFEK a Marija PAJDAKOVIĆ. Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice. arXiv. 2023, 2305.10116, 26 s.</citation>
    </results_reference>
    <verification_date>July 2023</verification_date>
    <study_first_submitted>July 12, 2023</study_first_submitted>
    <study_first_submitted_qc>July 19, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">July 27, 2023</study_first_posted>
    <last_update_submitted>July 19, 2023</last_update_submitted>
    <last_update_submitted_qc>July 19, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">July 27, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Computer-Aided Detection</keyword>
    <keyword>Deep Learning</keyword>
    <keyword>Chest X-ray</keyword>
    <keyword>Radiology</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Multiple Pulmonary Nodules</mesh_term>
      <mesh_term>Pleural Effusion</mesh_term>
      <mesh_term>Pulmonary Atelectasis</mesh_term>
      <mesh_term>Pneumothorax</mesh_term>
      <mesh_term>Solitary Pulmonary Nodule</mesh_term>
      <mesh_term>Cardiomegaly</mesh_term>
      <mesh_term>Emphysema</mesh_term>
      <mesh_term>Subcutaneous Emphysema</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05963945
  org study id: 00002
  nct id: NCT05963945
  lead sponsor: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot
        AI CXR) in comparison to individual radiologists.
  In the period between October 18th, 2022, and November 17th, 2022, anonymized chest X-ray
        images of patients were collected at the Radiodiagnostic Department of the Havířov Hospital,
        p.o. The collection process involved utilizing the CloudPACS imaging and archiving system
        provided by OR-CZ spol. s r.o.
  
        The collected X-ray images were subjected to the proposed DLAD (Carebot AI CXR) for analysis.
        Subsequently, the DLAD's performance was compared with the standard clinical practice, where
        radiologists assessed the CXR images in the simulated hospital setting with access to
        standard viewing tools (e.g., pan, zoom, WW/WL) and were given an unlimited amount of time to
        complete the review. Each radiologist determined the presence or absence of 7 indicated
        radiological findings, including atelectasis (ATE), consolidation (CON), pleural effusion
        (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and
        pneumothorax (PNO).
  observational model: Cohort
  time perspective: Retrospective
  measure: Performance test
  time frame: March 2023
  description: The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot AI CXR) in comparison to individual radiologists. The performance test includes sensitivity and specificity, positive and negative likelihood ratio, and positive and negative predictive value. The aforementioned parameters are statistically compared using confidence intervals (CI) and p-Values. The comparison procedure consists of two steps: a global hypothesis test is conducted to determine whether there are significant differences between DLAD and radiologists. If the global hypothesis test yields a significant result, individual hypothesis tests are performed. Additionally, multiple comparison methods, such as McNemar with continuity correction for Se and Sp, Holm method for LRs, and weighted generalized score statistics for PVs, are applied to control the overall error rate. All tests are performed as two-tailed tests at the 5% significance level.
  arm group label: Retrospective collection for the period October 18th, 2022, and November 17th, 2022
  description: A total of 1,073 chest X-rays were acquired within the specified period at the department. The data collection remained intact and unaffected throughout the testing phase, ensuring the integrity of the dataset. The collected sample accurately represents the prevalence of findings within the observed population. After excluding ineligible studies such as X-rays from patients under 18 years of age, lateral projection X-rays, and scans of insufficient quality, a total of 956 relevant CXRs were identified for further assessment.
  intervention type: Device
  intervention name: Carebot AI CXR
  description: The proposed DLAD (Carebot AI CXR) is a deep learning-based medical device designed to assist radiologists in interpreting chest X-ray images acquired in anteroposterior (AP) or posteroanterior (PA) projection. By employing advanced deep learning algorithms, this solution enables automatic detection of abnormal findings by analyzing visual patterns associated with specific conditions. The targeted abnormalities include atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and pneumothorax (PNO). The DLAD functions as a prediction algorithm complemented by various application peripherals, such as web-based communication tools, DICOM file conversion capabilities, and storage and reporting libraries supporting both DICOM Structured Report and DICOM Presentation State formats.
  arm group label: Retrospective collection for the period October 18th, 2022, and November 17th, 2022
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  facility: 
  country: Czechia
  citation: KVAK, Daniel, Anna CHROMCOVÁ, Petra OVESNÁ, Jakub DANDÁR, Marek BIROŠ, Robert HRUBÝ, Daniel DUFEK a Marija PAJDAKOVIĆ. Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice. arXiv. 2023, 2305.10116, 26 s.
  responsible party type: Sponsor
  mesh term: Multiple Pulmonary Nodules
  mesh term: Pleural Effusion
  mesh term: Pulmonary Atelectasis
  mesh term: Pneumothorax
  mesh term: Solitary Pulmonary Nodule
  mesh term: Cardiomegaly
  mesh term: Emphysema
  mesh term: Subcutaneous Emphysema
  sharing ipd: No