<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/NCT04489992</url>
  </required_header>
  <id_info>
    <org_study_id>2020-3</org_study_id>
    <nct_id>NCT04489992</nct_id>
  </id_info>
  <brief_title>Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System</brief_title>
  <official_title>Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department</source>
  <oversight_info>
    <has_dmc>No</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>
      It is planned to integrate various services based on computer vision technologies for&#xD;
      analysis of the certain type of x-ray study into Moscow Unified Radiological Information&#xD;
      Service (hereinafter referred to as URIS).&#xD;
&#xD;
      As a result of using computer vision-based services, it is expected:&#xD;
&#xD;
        1. Reducing the number of false negative and false positive diagnoses;&#xD;
&#xD;
        2. Reducing the time between conducting a study and obtaining a report by the referring&#xD;
           physician;&#xD;
&#xD;
        3. Increasing the average number of radiology reports provided by a radiologist per shift.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Recently a growth in the number of radiology studies across multiple modalities has been&#xD;
      observed alongside the modest increase in staffing levels. This carries higher risks of&#xD;
      increased workload and efficiency losses. The integration of computer vision-based services&#xD;
      into URIS will improve the radiologists' productivity and job performance.&#xD;
&#xD;
      Existing prerequisites for conducting the study:&#xD;
&#xD;
        1. Increasing the number of preventive and diagnostic radiological studies entails the&#xD;
           growing workload for radiologists and increased risk of interpretation errors, which in&#xD;
           turn leads to the decrease in quality of medical care.&#xD;
&#xD;
        2. When a radiologist opens a worklist of studies, in the absence of special notes, he/she&#xD;
           writes a report in the random order, not being able to select from the list the studies&#xD;
           that require the most attention and prompt response (studies with pathological&#xD;
           findings), which increases the time of diagnosis.&#xD;
&#xD;
        3. The absence of the structured pre-filled template of report leads to the increase in&#xD;
           time for preparing reports.&#xD;
&#xD;
        4. A radiologist has to spend considerable time evaluating the dynamics of pathological&#xD;
           changes, which also increases the time to prepare a report as well as the risk of error.&#xD;
&#xD;
        5. Interpretation of preventive studies requires double reading, which is implemented&#xD;
           inefficiently due to the staff shortage.&#xD;
&#xD;
      Study objectives:&#xD;
&#xD;
        1. Study the diagnostic accuracy of the Services in accordance with the methodological&#xD;
           guidelines No. 43 &quot;Clinical trials of software based on intelligent technologies&#xD;
           (diagnostic radiology)&quot; (recommended by the Expert Council on Science of the Moscow&#xD;
           Healthcare Department, Protocol No. 8 of June 25, 2019).&#xD;
&#xD;
        2. Audit the studies conducted with Services application in order to determine the number&#xD;
           of interpretation errors, and compare it with the audit result without their application&#xD;
           (hypothesis 1).&#xD;
&#xD;
        3. Conduct timekeeping to estimate time for preparing a report and the total number of&#xD;
           evaluated studies with and without using the Services (hypothesis 2,3).&#xD;
&#xD;
        4. Conduct a survey of radiologists who use the Services in their work, in order to&#xD;
           determine their opinion about the implementation of innovative technologies in the&#xD;
           diagnostic process.&#xD;
&#xD;
      METHODOLOGY&#xD;
&#xD;
        1. The Experiment is carried out by the Moscow Healthcare Department in accordance with&#xD;
           Regulation No. 43 of January 24, 2020 &quot;On approval of the procedure and conditions for&#xD;
           conducting the Experiment on the use of innovative computer vision technologies for&#xD;
           analysis of medical images and further application in Moscow healthcare system&quot;.&#xD;
&#xD;
        2. The experiment is conducted on the next types of studies:&#xD;
&#xD;
      1)Detection of CT signs consistent with COVID-19 (coronavirus) lung involvement (Chest CT);&#xD;
      2) Emphysema extent (Chest CT); 3) Detection of CT signs consistent with malignant neoplasm&#xD;
      in the lungs (Chest CT); 4) Detection of LDCT signs consistent with malignant neoplasm in the&#xD;
      lungs (Chest LDCT); 5) Detection and localization of compression vertebral fractures with a&#xD;
      degree of vertebral body deformity of over 25% according to the Genant semi-quantitative&#xD;
      scale, grades 2-3 (Chest CT); 6) Detection of free pleural fluid (effusion) (Chest CT); 7)&#xD;
      Detection of enlarged intrathoracic lymph nodes (lymphadenopathy) (Chest CT); 8) Detection of&#xD;
      bronchiectasis (Chest CT); 9) Detection of CT signs consistent with pulmonary tuberculosis&#xD;
      (Chest CT); 10) Coronary calcium score (Chest CT/ LDCT); 11) Paricardial fat volume (Chest&#xD;
      CT); 12) Dilation of ascending and descending thoracic aortas (Chest CT/ LDCT); 13) Dilation&#xD;
      of the pulmonary trunk (Chest CT/ LDCT); 14) Detection of sarcoidosis (Chest CT); 15)&#xD;
      Detection of signs consistent with the impairment of lung airness (Chest CT); 16) Detection&#xD;
      of signs consistent with the focal lesions in the chest bones (Chest CT); 17) Detection of CT&#xD;
      signs consistent with rib fracture (Chest CT); 18) Detection of signs of urolithiasis&#xD;
      (Abdominal CT); 19) Detection of signs consistent with the focal lesions in the skeleton&#xD;
      bones (Abdominal CT); 20) Detection of liver lesions (Abdominal CT); 21) Detection of CT&#xD;
      signs consistent with gallbladder stones (Abdominal CT); 22) Detection of CT signs consistent&#xD;
      with renal lesions (Abdominal CT); 23) Measuring the abdominal aorta dilation (Abdominal CT);&#xD;
      24) Detection of adrenal lesions (Abdominal/Chest CT); 25) Detection and localization of&#xD;
      compression vertebral fractures with a degree of vertebral body deformity of over 25%&#xD;
      according to the Genant semi-quantitative scale, grades 2-3 (Abdominal CT); 26) Automation of&#xD;
      routine liver measurements (dimensions, liver density, choledochus diameter, portal vein&#xD;
      diameter) (Abdominal CT); 27) Automation of routine kidney measurements (kidney size,&#xD;
      pelvicalyceal system size) (Abdominal CT); 28) Automation of routine measurements of spleen&#xD;
      and pancreas (size, density of the spleen and pancreas) (Abdominal CT); 29) Detection of&#xD;
      acute ischemic stroke and its ASPECTS score (Head CT); 30) Detection of hemorrhage and its&#xD;
      automatic volume calculation in ml or cm³ (Head CT); 31) Automation of routine measurements&#xD;
      (ventriculometry, displacement of median structures, measurement of the craniovertebral&#xD;
      junction) (Head CT); 32) Detection and localization of (at least 7) signs consistent with the&#xD;
      priority disease (Chest XR); 33) Detection of signs (at least one) consistent with bone&#xD;
      fracture (MSS XR); 34) Detection of radiologic signs (at least one) consistent with arthrosis&#xD;
      of the joints (MSS XR); 35) Detection of radiological signs (at least one) consistent with&#xD;
      deforming arthrosis of the hip (MSS XR); 36) Detection of radiological signs (at least one)&#xD;
      consistent with the fracture of the shoulder joint bones (MSS XR); 37) Detection of&#xD;
      radiological signs (at least one) consistent with the fracture of the hip joint bones (MSS&#xD;
      XR) 38) Detection of radiological signs (at least one) consistent with the fracture of the&#xD;
      ankle joint bones (MSS XR).&#xD;
&#xD;
      39) Detection of reduced pneumatization / opacity of the paranasal sinuses (Head XR) 40)&#xD;
      Detection of signs (at least one) consistent with transverse flat foot (MSS XR) 41) Detection&#xD;
      of signs (at least one) consistent with the longitudinal flat foot in the lateral plane (MSS&#xD;
      XR); 42) Detection of the signs of osteoporosis: detection and localization of compression&#xD;
      vertebral fractures with a degree of height loss of over 25% as well as the radio density&#xD;
      measurements of vertebral bodies (Spine XR); 43) Detection of signs consistent with&#xD;
      osteochondrosis in the frontal and/or sagittal plane (Spine XR); 44) Detection of signs&#xD;
      consistent with scoliosis in the frontal plane (Spine XR); 45) Detection of signs consistent&#xD;
      with spondylolisthesis in the sagittal plane (Spine XR); 46) Detection and localization of&#xD;
      findings consistent with breast cancer (MMG); 47) Detection of multiple sclerosis (Brain&#xD;
      MRI); 48) Detection and localization of intracranial neoplasms (extracerebral, intracerebral)&#xD;
      (Brain MRI); 49) Automation of routine measurements (ventriculometry, displacement of median&#xD;
      structures, measurement of the craniovertebral junction, changes in white matter,&#xD;
      intracranial measurements) (Brain MRI); 50) Detection of signs consistent with the focal&#xD;
      lesions in the cervical spinal cord (Cervical spine MRI); 51) Detection and localization of&#xD;
      MRI signs (at least one) consistent with degenerative changes in the cervical discs on&#xD;
      sagittal and axial T2-WI (Cervical spine MRI); 52) Detection and localization of MRI signs&#xD;
      (at least one) consistent with degenerative changes in the thoracic discs on sagittal and&#xD;
      axial T2-WI (Thoracic spine MRI); 53) Detection of signs consistent with the focal lesions in&#xD;
      the thoracic spinal cord (Thoracic spine MRI); 54) Detection and localization of MRI signs&#xD;
      (at least one) consistent with degenerative changes in the lumbosacral discs on sagittal and&#xD;
      axial T2-WI (Lumbosacral spine MRI); 55) Detection of signs consistent with the focal lesions&#xD;
      in the lumbosacral spinal cord (Lumbosacral spine MRI); 56) Detecting signs consistent with&#xD;
      the areas of cartilage breakdown (chondromalacia) along the articular surfaces of the knee&#xD;
      and the patellofemoral joint (Knee joint MRI); 57) Automated routine measurements of the&#xD;
      prostate gland (dimensions) (Lesser pelvis MRI); 58) Automated routine measurements of the&#xD;
      uterus (corpus and cervix: position, dimensions, displacements) (Lesser pelvis MRI).&#xD;
&#xD;
      3. For each Service during the Experiment, a certain number of studies is provided for&#xD;
      processing based on their type:&#xD;
&#xD;
        1. CT/LDCT - 30 250 studies;&#xD;
&#xD;
        2. XR - 55 000 studies;&#xD;
&#xD;
        3. MMG - 48 500 studies;&#xD;
&#xD;
        4. MRI - 22 500 studies.&#xD;
&#xD;
      4. A methodology for including services in the Experiment has been developed. For each&#xD;
      Service, the participation process in the Experiment consists of the following stages:&#xD;
&#xD;
        1. selection;&#xD;
&#xD;
        2. the preparatory stage;&#xD;
&#xD;
        3. the main stage;&#xD;
&#xD;
        4. the final stage.&#xD;
&#xD;
      During the Experiment, a radiologist will routinely be able to:&#xD;
&#xD;
        -  work on a sorted list of patients (triage);&#xD;
&#xD;
        -  work with images processed by the Service;&#xD;
&#xD;
        -  work with a pre-filled template of the radiological report on each study;&#xD;
&#xD;
        -  evaluate the work of the Service according to the developed questionnaire. During the&#xD;
           Experiment, a patient will receive the individual plan of the follow-up support. It&#xD;
           includes preventive examinations or observation as well as treatment by a specialist.&#xD;
&#xD;
      Systematization and final analysis of the Experiment results is carried out within three&#xD;
      months from the completion date of the last Service participation in the Experiment.&#xD;
&#xD;
      Based on the results of the Experiment, recommendations can be prepared on the possibility to&#xD;
      register certain services as a medical device (software).&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Recruiting</overall_status>
  <start_date type="Actual">February 21, 2020</start_date>
  <completion_date type="Anticipated">January 1, 2024</completion_date>
  <primary_completion_date type="Anticipated">January 1, 2024</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Crossover</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Number of errors</measure>
    <time_frame>Upon completion, up to 4 years</time_frame>
    <description>Change of at least 30% in the number of errors in interpretation of the studies with using computer vision-based services compared to the number of errors in interpretation without their application.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Report turnaround time</measure>
    <time_frame>Upon completion, up to 3 year</time_frame>
    <description>Change of at least 30% of the time from the study completion to report finalization by a radiologist.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of reports</measure>
    <time_frame>Upon completion, up to 4 years</time_frame>
    <description>Change of at least 30% in the number of radiology reports provided by a radiologist per shift.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Change in the errors of services per the feedback form</measure>
    <time_frame>Upon completion, up to 4 years</time_frame>
    <description>Change of at least 30% in computer vision-based services errors as per integrated feedback form for radiologists in the PACS.&#xD;
Types of errors:&#xD;
Technological defect (absent AI-generated series, partially generated AI series, DICOM SR and images mismatch, multiple conflicting results)&#xD;
Major discrepancy (findings outside the region of interest, irrelevant findings)&#xD;
Inaccurate diagnosis&#xD;
Inaccurate lesion localisation&#xD;
Inaccurate lesion classification&#xD;
Other (free-text field)</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Anticipated">133000</enrollment>
  <condition>AI (Artificial Intelligence)</condition>
  <condition>Mammary Cancer</condition>
  <condition>Lung Cancer</condition>
  <condition>X-Rays; Lesion</condition>
  <condition>COVID-19</condition>
  <condition>Chest--Diseases</condition>
  <condition>Abdomen Disease</condition>
  <condition>Brain Disease</condition>
  <condition>Fractures, Bone</condition>
  <arm_group>
    <arm_group_label>Standard radiology studies with AI</arm_group_label>
    <description>The experiment is conducted on 10 types of studies with AI:&#xD;
Chest CT/ LDCT with different pathologies;&#xD;
Abdominal CT with different pathologies;&#xD;
Head CT with different pathologies;&#xD;
MSS XR with different fractures&#xD;
Spine XR with different pathologies;&#xD;
MMG;&#xD;
Brain MRI with different pathologies;&#xD;
Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies&#xD;
Knee joint MRI&#xD;
Lesser pelvis MRI.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Standard radiology studies without AI</arm_group_label>
    <description>The experiment is conducted on 10 types of studies without AI:&#xD;
Chest CT/ LDCT with different pathologies;&#xD;
Abdominal CT with different pathologies;&#xD;
Head CT with different pathologies;&#xD;
MSS XR with different fractures&#xD;
Spine XR with different pathologies;&#xD;
MMG;&#xD;
Brain MRI with different pathologies;&#xD;
Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies&#xD;
Knee joint MRI&#xD;
Lesser pelvis MRI.</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        patients over the age of 18 attending outpatient clinics&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age (over 18 years)&#xD;
&#xD;
          -  Gender (male and female)&#xD;
&#xD;
          -  Referral for the study&#xD;
&#xD;
          -  Signed informed consent to participate in the Experiment&#xD;
&#xD;
          -  Chest computed tomography and Low-dose computed tomography for lung cancer detection&#xD;
             or mammography for breast cancer detection or chest X-ray for lung pathology detection&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Another type of study (including a different modality and anatomical area)&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Anton Vladzymyrskyy</last_name>
    <role>Study Director</role>
    <affiliation>Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Kirill Arzamasov, PhD</last_name>
    <phone>+79152514838</phone>
    <email>ArzamasovKM@zdrav.mos.ru</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Olga Omelyanskaya</last_name>
    <phone>+79263948149</phone>
    <email>OmelyanskayaOV@zdrav.mos.ru</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department</name>
      <address>
        <city>Moscow</city>
        <country>Russian Federation</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>PhD</last_name>
    </contact>
    <contact_backup>
      <last_name>Kirill Arzamasov, PhD</last_name>
      <phone>+79152514838</phone>
      <email>ArzamasovKM@zdrav.mos.ru</email>
    </contact_backup>
  </location>
  <location_countries>
    <country>Russian Federation</country>
  </location_countries>
  <link>
    <url>http://mosmed.ai</url>
    <description>Experiment on the use of innovative computer vision technologies for analysis of medical images in the Moscow healthcare system</description>
  </link>
  <verification_date>May 2023</verification_date>
  <study_first_submitted>July 17, 2020</study_first_submitted>
  <study_first_submitted_qc>July 24, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 28, 2020</study_first_posted>
  <last_update_submitted>May 25, 2023</last_update_submitted>
  <last_update_submitted_qc>May 25, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">May 26, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>AI (Artificial Intelligence)</keyword>
  <keyword>Computed tomography</keyword>
  <keyword>Low-dose CT</keyword>
  <keyword>X-ray chest</keyword>
  <keyword>mammography</keyword>
  <keyword>COVID-19</keyword>
  <keyword>Chest--Diseases</keyword>
  <keyword>Abdomen Disease</keyword>
  <keyword>Brain Disease</keyword>
  <keyword>Fractures, Bone</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>COVID-19</mesh_term>
    <mesh_term>Breast Neoplasms</mesh_term>
    <mesh_term>Brain Diseases</mesh_term>
    <mesh_term>Fractures, Bone</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


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/NCT04489992
org study id: 2020-3
nct id: NCT04489992
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
It is planned to integrate various services based on computer vision technologies for
      analysis of the certain type of x-ray study into Moscow Unified Radiological Information
      Service (hereinafter referred to as URIS).

      As a result of using computer vision-based services, it is expected:

        1. Reducing the number of false negative and false positive diagnoses;

        2. Reducing the time between conducting a study and obtaining a report by the referring
           physician;

        3. Increasing the average number of radiology reports provided by a radiologist per shift.
Recently a growth in the number of radiology studies across multiple modalities has been
      observed alongside the modest increase in staffing levels. This carries higher risks of
      increased workload and efficiency losses. The integration of computer vision-based services
      into URIS will improve the radiologists' productivity and job performance.

      Existing prerequisites for conducting the study:

        1. Increasing the number of preventive and diagnostic radiological studies entails the
           growing workload for radiologists and increased risk of interpretation errors, which in
           turn leads to the decrease in quality of medical care.

        2. When a radiologist opens a worklist of studies, in the absence of special notes, he/she
           writes a report in the random order, not being able to select from the list the studies
           that require the most attention and prompt response (studies with pathological
           findings), which increases the time of diagnosis.

        3. The absence of the structured pre-filled template of report leads to the increase in
           time for preparing reports.

        4. A radiologist has to spend considerable time evaluating the dynamics of pathological
           changes, which also increases the time to prepare a report as well as the risk of error.

        5. Interpretation of preventive studies requires double reading, which is implemented
           inefficiently due to the staff shortage.

      Study objectives:

        1. Study the diagnostic accuracy of the Services in accordance with the methodological
           guidelines No. 43 "Clinical trials of software based on intelligent technologies
           (diagnostic radiology)" (recommended by the Expert Council on Science of the Moscow
           Healthcare Department, Protocol No. 8 of June 25, 2019).

        2. Audit the studies conducted with Services application in order to determine the number
           of interpretation errors, and compare it with the audit result without their application
           (hypothesis 1).

        3. Conduct timekeeping to estimate time for preparing a report and the total number of
           evaluated studies with and without using the Services (hypothesis 2,3).

        4. Conduct a survey of radiologists who use the Services in their work, in order to
           determine their opinion about the implementation of innovative technologies in the
           diagnostic process.

      METHODOLOGY

        1. The Experiment is carried out by the Moscow Healthcare Department in accordance with
           Regulation No. 43 of January 24, 2020 "On approval of the procedure and conditions for
           conducting the Experiment on the use of innovative computer vision technologies for
           analysis of medical images and further application in Moscow healthcare system".

        2. The experiment is conducted on the next types of studies:

      1)Detection of CT signs consistent with COVID-19 (coronavirus) lung involvement (Chest CT);
      2) Emphysema extent (Chest CT); 3) Detection of CT signs consistent with malignant neoplasm
      in the lungs (Chest CT); 4) Detection of LDCT signs consistent with malignant neoplasm in the
      lungs (Chest LDCT); 5) Detection and localization of compression vertebral fractures with a
      degree of vertebral body deformity of over 25% according to the Genant semi-quantitative
      scale, grades 2-3 (Chest CT); 6) Detection of free pleural fluid (effusion) (Chest CT); 7)
      Detection of enlarged intrathoracic lymph nodes (lymphadenopathy) (Chest CT); 8) Detection of
      bronchiectasis (Chest CT); 9) Detection of CT signs consistent with pulmonary tuberculosis
      (Chest CT); 10) Coronary calcium score (Chest CT/ LDCT); 11) Paricardial fat volume (Chest
      CT); 12) Dilation of ascending and descending thoracic aortas (Chest CT/ LDCT); 13) Dilation
      of the pulmonary trunk (Chest CT/ LDCT); 14) Detection of sarcoidosis (Chest CT); 15)
      Detection of signs consistent with the impairment of lung airness (Chest CT); 16) Detection
      of signs consistent with the focal lesions in the chest bones (Chest CT); 17) Detection of CT
      signs consistent with rib fracture (Chest CT); 18) Detection of signs of urolithiasis
      (Abdominal CT); 19) Detection of signs consistent with the focal lesions in the skeleton
      bones (Abdominal CT); 20) Detection of liver lesions (Abdominal CT); 21) Detection of CT
      signs consistent with gallbladder stones (Abdominal CT); 22) Detection of CT signs consistent
      with renal lesions (Abdominal CT); 23) Measuring the abdominal aorta dilation (Abdominal CT);
      24) Detection of adrenal lesions (Abdominal/Chest CT); 25) Detection and localization of
      compression vertebral fractures with a degree of vertebral body deformity of over 25%
      according to the Genant semi-quantitative scale, grades 2-3 (Abdominal CT); 26) Automation of
      routine liver measurements (dimensions, liver density, choledochus diameter, portal vein
      diameter) (Abdominal CT); 27) Automation of routine kidney measurements (kidney size,
      pelvicalyceal system size) (Abdominal CT); 28) Automation of routine measurements of spleen
      and pancreas (size, density of the spleen and pancreas) (Abdominal CT); 29) Detection of
      acute ischemic stroke and its ASPECTS score (Head CT); 30) Detection of hemorrhage and its
      automatic volume calculation in ml or cm³ (Head CT); 31) Automation of routine measurements
      (ventriculometry, displacement of median structures, measurement of the craniovertebral
      junction) (Head CT); 32) Detection and localization of (at least 7) signs consistent with the
      priority disease (Chest XR); 33) Detection of signs (at least one) consistent with bone
      fracture (MSS XR); 34) Detection of radiologic signs (at least one) consistent with arthrosis
      of the joints (MSS XR); 35) Detection of radiological signs (at least one) consistent with
      deforming arthrosis of the hip (MSS XR); 36) Detection of radiological signs (at least one)
      consistent with the fracture of the shoulder joint bones (MSS XR); 37) Detection of
      radiological signs (at least one) consistent with the fracture of the hip joint bones (MSS
      XR) 38) Detection of radiological signs (at least one) consistent with the fracture of the
      ankle joint bones (MSS XR).

      39) Detection of reduced pneumatization / opacity of the paranasal sinuses (Head XR) 40)
      Detection of signs (at least one) consistent with transverse flat foot (MSS XR) 41) Detection
      of signs (at least one) consistent with the longitudinal flat foot in the lateral plane (MSS
      XR); 42) Detection of the signs of osteoporosis: detection and localization of compression
      vertebral fractures with a degree of height loss of over 25% as well as the radio density
      measurements of vertebral bodies (Spine XR); 43) Detection of signs consistent with
      osteochondrosis in the frontal and/or sagittal plane (Spine XR); 44) Detection of signs
      consistent with scoliosis in the frontal plane (Spine XR); 45) Detection of signs consistent
      with spondylolisthesis in the sagittal plane (Spine XR); 46) Detection and localization of
      findings consistent with breast cancer (MMG); 47) Detection of multiple sclerosis (Brain
      MRI); 48) Detection and localization of intracranial neoplasms (extracerebral, intracerebral)
      (Brain MRI); 49) Automation of routine measurements (ventriculometry, displacement of median
      structures, measurement of the craniovertebral junction, changes in white matter,
      intracranial measurements) (Brain MRI); 50) Detection of signs consistent with the focal
      lesions in the cervical spinal cord (Cervical spine MRI); 51) Detection and localization of
      MRI signs (at least one) consistent with degenerative changes in the cervical discs on
      sagittal and axial T2-WI (Cervical spine MRI); 52) Detection and localization of MRI signs
      (at least one) consistent with degenerative changes in the thoracic discs on sagittal and
      axial T2-WI (Thoracic spine MRI); 53) Detection of signs consistent with the focal lesions in
      the thoracic spinal cord (Thoracic spine MRI); 54) Detection and localization of MRI signs
      (at least one) consistent with degenerative changes in the lumbosacral discs on sagittal and
      axial T2-WI (Lumbosacral spine MRI); 55) Detection of signs consistent with the focal lesions
      in the lumbosacral spinal cord (Lumbosacral spine MRI); 56) Detecting signs consistent with
      the areas of cartilage breakdown (chondromalacia) along the articular surfaces of the knee
      and the patellofemoral joint (Knee joint MRI); 57) Automated routine measurements of the
      prostate gland (dimensions) (Lesser pelvis MRI); 58) Automated routine measurements of the
      uterus (corpus and cervix: position, dimensions, displacements) (Lesser pelvis MRI).

      3. For each Service during the Experiment, a certain number of studies is provided for
      processing based on their type:

        1. CT/LDCT - 30 250 studies;

        2. XR - 55 000 studies;

        3. MMG - 48 500 studies;

        4. MRI - 22 500 studies.

      4. A methodology for including services in the Experiment has been developed. For each
      Service, the participation process in the Experiment consists of the following stages:

        1. selection;

        2. the preparatory stage;

        3. the main stage;

        4. the final stage.

      During the Experiment, a radiologist will routinely be able to:

        -  work on a sorted list of patients (triage);

        -  work with images processed by the Service;

        -  work with a pre-filled template of the radiological report on each study;

        -  evaluate the work of the Service according to the developed questionnaire. During the
           Experiment, a patient will receive the individual plan of the follow-up support. It
           includes preventive examinations or observation as well as treatment by a specialist.

      Systematization and final analysis of the Experiment results is carried out within three
      months from the completion date of the last Service participation in the Experiment.

      Based on the results of the Experiment, recommendations can be prepared on the possibility to
      register certain services as a medical device (software).
observational model: Case-Crossover
time perspective: Prospective
measure: Number of errors
time frame: Upon completion, up to 4 years
description: Change of at least 30% in the number of errors in interpretation of the studies with using computer vision-based services compared to the number of errors in interpretation without their application.
measure: Report turnaround time
time frame: Upon completion, up to 3 year
description: Change of at least 30% of the time from the study completion to report finalization by a radiologist.
measure: Number of reports
time frame: Upon completion, up to 4 years
description: Change of at least 30% in the number of radiology reports provided by a radiologist per shift.
measure: Change in the errors of services per the feedback form
time frame: Upon completion, up to 4 years
description: Change of at least 30% in computer vision-based services errors as per integrated feedback form for radiologists in the PACS.
Types of errors:
Technological defect (absent AI-generated series, partially generated AI series, DICOM SR and images mismatch, multiple conflicting results)
Major discrepancy (findings outside the region of interest, irrelevant findings)
Inaccurate diagnosis
Inaccurate lesion localisation
Inaccurate lesion classification
Other (free-text field)
arm group label: Standard radiology studies with AI
description: The experiment is conducted on 10 types of studies with AI:
Chest CT/ LDCT with different pathologies;
Abdominal CT with different pathologies;
Head CT with different pathologies;
MSS XR with different fractures
Spine XR with different pathologies;
MMG;
Brain MRI with different pathologies;
Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies
Knee joint MRI
Lesser pelvis MRI.
arm group label: Standard radiology studies without AI
description: The experiment is conducted on 10 types of studies without AI:
Chest CT/ LDCT with different pathologies;
Abdominal CT with different pathologies;
Head CT with different pathologies;
MSS XR with different fractures
Spine XR with different pathologies;
MMG;
Brain MRI with different pathologies;
Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies
Knee joint MRI
Lesser pelvis MRI.
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Anton Vladzymyrskyy
role: Study Director
affiliation: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
last name: Kirill Arzamasov, PhD
phone: +79152514838
email: ArzamasovKM@zdrav.mos.ru
last name: Olga Omelyanskaya
phone: +79263948149
email: OmelyanskayaOV@zdrav.mos.ru
facility: 
status: Recruiting
contact: 
contact backup: 
country: Russian Federation
url: http://mosmed.ai
description: Experiment on the use of innovative computer vision technologies for analysis of medical images in the Moscow healthcare system
responsible party type: Sponsor
mesh term: COVID-19
mesh term: Breast Neoplasms
mesh term: Brain Diseases
mesh term: Fractures, Bone
sharing ipd: No