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Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System

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ClinicalTrials.gov Identifier: NCT04489992
Recruitment Status : Recruiting
First Posted : July 28, 2020
Last Update Posted : July 29, 2021
Sponsor:
Information provided by (Responsible Party):
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Tracking Information
First Submitted Date July 17, 2020
First Posted Date July 28, 2020
Last Update Posted Date July 29, 2021
Actual Study Start Date February 21, 2020
Estimated Primary Completion Date January 1, 2023   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: July 24, 2020)
Number of errors [ Time Frame: Upon completion, up to 1 year ]
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.
Original Primary Outcome Measures Same as current
Change History
Current Secondary Outcome Measures
 (submitted: August 4, 2020)
  • Report turnaround time [ Time Frame: Upon completion, up to 1 year ]
    Change of at least 30% of the time from the study completion to report finalization by a radiologist.
  • Number of reports [ Time Frame: Upon completion, up to 1 year ]
    Change of at least 30% in the number of radiology reports provided by a radiologist per shift.
  • Change in the errors of services per the feedback form [ Time Frame: Upon completion, up to 1 year ]
    Change of at least 30% in computer vision-based services errors as per integrated feedback form for radiologists in the PACS. Types of errors:
    1. Technological defect (absent AI-generated series, partially generated AI series, DICOM SR and images mismatch, multiple conflicting results)
    2. Major discrepancy (findings outside the region of interest, irrelevant findings)
    3. Inaccurate diagnosis
    4. Inaccurate lesion localisation
    5. Inaccurate lesion classification
    6. Other (free-text field)
Original Secondary Outcome Measures
 (submitted: July 24, 2020)
  • Report turnaround time [ Time Frame: Upon completion, up to 1 year ]
    Change of at least 30% of the time from the study completion to report finalization by a radiologist.
  • Number of reports [ Time Frame: Upon completion, up to 1 year ]
    Change of at least 30% in the number of radiology reports provided by a radiologist per shift.
  • Feedback from radiologists [ Time Frame: Upon completion, up to 1 year ]
    Change of at least 30% in computer vision-based services acceptance and satisfaction as per periodic questionnaires for radiologists.
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System
Official Title Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System
Brief Summary

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.
Detailed Description

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 three types of studies:

1) Chest Computed tomography and Low-Dose Computed Tomography for lung cancer detection (hereinafter referred to as CT/LDCT); 2) Chest X-ray for lung pathology detection (hereinafter referred to as XR); 3) Mammography for breast cancer detection (hereinafter referred to as ММГ).

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. 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).

Study Type Observational
Study Design Observational Model: Case-Crossover
Time Perspective: Prospective
Target Follow-Up Duration Not Provided
Biospecimen Not Provided
Sampling Method Non-Probability Sample
Study Population patients over the age of 18 attending outpatient clinics
Condition
  • AI (Artificial Intelligence)
  • Mammary Cancer
  • Lung Cancer
  • X-Rays; Lesion
Intervention Not Provided
Study Groups/Cohorts
  • Standard radiology studies with AI

    The experiment is conducted on three types of studies:

    1. Chest Computed tomography and Low-Dose Computed Tomography for lung cancer detection (hereinafter referred to as CT/LDCT) with artificial intelligence results;
    2. Chest X-ray for lung pathology detection (hereinafter referred to as XR) with artificial intelligence results;
    3. Mammography for breast cancer detection (hereinafter referred to as MG) with artificial intelligence results;
  • Standard radiology studies without AI

    The experiment is conducted on three types of studies:

    1. Chest Computed tomography and Low-Dose Computed Tomography for lung cancer detection (hereinafter referred to as CT/LDCT) without artificial intelligence results;
    2. Chest X-ray for lung pathology detection (hereinafter referred to as XR) without artificial intelligence results;
    3. Mammography for breast cancer detection (hereinafter referred to as MG) without artificial intelligence results;
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Recruiting
Estimated Enrollment
 (submitted: July 24, 2020)
133000
Original Estimated Enrollment Same as current
Estimated Study Completion Date December 31, 2023
Estimated Primary Completion Date January 1, 2023   (Final data collection date for primary outcome measure)
Eligibility Criteria

Inclusion Criteria:

  • Age (over 18 years)
  • Gender (male and female)
  • Referral for the study
  • Signed informed consent to participate in the Experiment
  • Chest computed tomography and Low-dose computed tomography for lung cancer detection or mammography for breast cancer detection or chest X-ray for lung pathology detection

Exclusion Criteria:

  • Another type of study (including a different modality and anatomical area)
Sex/Gender
Sexes Eligible for Study: All
Ages 18 Years and older   (Adult, Older Adult)
Accepts Healthy Volunteers Yes
Contacts
Contact: Anna Andreychenko, PhD +79163212570 a.andreychenko@npcmr.ru
Contact: Victor Gombolevskiy, PhD +79263948149 gombolevskiy@npcmr.ru
Listed Location Countries Russian Federation
Removed Location Countries  
 
Administrative Information
NCT Number NCT04489992
Other Study ID Numbers 2020-3
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: No
Responsible Party Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Study Sponsor Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Collaborators Not Provided
Investigators
Study Director: Sergey Morozov Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
PRS Account Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Verification Date July 2020