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Trial record 1 of 1 for:    NCT04489992
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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 : August 6, 2020
Sponsor:
Information provided by (Responsible Party):
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

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.

Condition or disease
AI (Artificial Intelligence) Mammary Cancer Lung Cancer X-Rays; Lesion

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Study Type : Observational
Estimated Enrollment : 133000 participants
Observational Model: Case-Crossover
Time Perspective: Prospective
Official Title: Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System
Actual Study Start Date : February 21, 2020
Estimated Primary Completion Date : December 31, 2020
Estimated Study Completion Date : December 31, 2020

Resource links provided by the National Library of Medicine


Group/Cohort
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;



Primary Outcome Measures :
  1. 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.


Secondary Outcome Measures :
  1. 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.

  2. 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.

  3. 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)



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
patients over the age of 18 attending outpatient clinics
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)

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04489992


Contacts
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Contact: Anna Andreychenko, PhD +31 6 24218452 a.andreychenko@npcmr.ru
Contact: Victor Gombolevskiy, PhD +79263948149 gombolevskiy@npcmr.ru

Locations
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Russian Federation
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department Recruiting
Moscow, Russian Federation
Contact: Victor Gombolevskiy, PhD    +79263948149    g_victor@mail.ru   
Contact: Anna Andreychenko, PhD    +31 6 24218452    a.andreychenko@npcmr.ru   
Sponsors and Collaborators
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Investigators
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Study Director: Sergey Morozov Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Additional Information:
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Responsible Party: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
ClinicalTrials.gov Identifier: NCT04489992    
Other Study ID Numbers: 2020-3
First Posted: July 28, 2020    Key Record Dates
Last Update Posted: August 6, 2020
Last Verified: July 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department:
AI (Artificial Intelligence)
Computed tomography
Low-dose CT
X-ray chest
mammography
Additional relevant MeSH terms:
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Breast Neoplasms
Neoplasms by Site
Neoplasms
Breast Diseases
Skin Diseases