Don't get left behind! The modernized ClinicalTrials.gov is coming. Check it out now.
Say goodbye to ClinicalTrials.gov!
The new site is coming soon - go to the modernized ClinicalTrials.gov
Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04489992
Recruitment Status : Recruiting
First Posted : July 28, 2020
Last Update Posted : May 26, 2023
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 COVID-19 Chest--Diseases Abdomen Disease Brain Disease Fractures, Bone

Show Show detailed description

Layout table for study information
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 : January 1, 2024
Estimated Study Completion Date : January 1, 2024

Group/Cohort
Standard radiology studies with AI

The experiment is conducted on 10 types of studies with AI:

  1. Chest CT/ LDCT with different pathologies;
  2. Abdominal CT with different pathologies;
  3. Head CT with different pathologies;
  4. MSS XR with different fractures
  5. Spine XR with different pathologies;
  6. MMG;
  7. Brain MRI with different pathologies;
  8. Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies
  9. Knee joint MRI
  10. Lesser pelvis MRI.
Standard radiology studies without AI

The experiment is conducted on 10 types of studies without AI:

  1. Chest CT/ LDCT with different pathologies;
  2. Abdominal CT with different pathologies;
  3. Head CT with different pathologies;
  4. MSS XR with different fractures
  5. Spine XR with different pathologies;
  6. MMG;
  7. Brain MRI with different pathologies;
  8. Cervical spine MRI, Lumbosacral spine MR and Thoracic spine MRI with spine pathologies
  9. Knee joint MRI
  10. Lesser pelvis MRI.



Primary Outcome Measures :
  1. Number of errors [ Time Frame: Upon completion, up to 4 years ]
    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 3 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 4 years ]
    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 4 years ]

    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

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
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
Layout table for location contacts
Contact: Kirill Arzamasov, PhD +79152514838 ArzamasovKM@zdrav.mos.ru
Contact: Olga Omelyanskaya +79263948149 OmelyanskayaOV@zdrav.mos.ru

Locations
Layout table for location information
Russian Federation
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department Recruiting
Moscow, Russian Federation
Contact: PhD         
Contact: Kirill Arzamasov, PhD    +79152514838    ArzamasovKM@zdrav.mos.ru   
Sponsors and Collaborators
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Investigators
Layout table for investigator information
Study Director: Anton Vladzymyrskyy Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Additional Information:
Layout table for additonal information
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: May 26, 2023
Last Verified: May 2023
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

Layout table for additional information
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
COVID-19
Chest--Diseases
Abdomen Disease
Brain Disease
Fractures, Bone
Additional relevant MeSH terms:
Layout table for MeSH terms
COVID-19
Breast Neoplasms
Brain Diseases
Fractures, Bone
Pneumonia, Viral
Pneumonia
Respiratory Tract Infections
Infections
Virus Diseases
Coronavirus Infections
Coronaviridae Infections
Nidovirales Infections
RNA Virus Infections
Lung Diseases
Respiratory Tract Diseases
Wounds and Injuries
Central Nervous System Diseases
Nervous System Diseases
Neoplasms by Site
Neoplasms
Breast Diseases
Skin Diseases