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

NCT04489992 | Unknown

• 1 other identifier: 2020-3
• Study Type: observational
• Target: 133,000
• Locations: 1 country
• Sites: 1

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. 1.Reducing the number of false negative and false positive diagnoses;
2. 2.Reducing the time between conducting a study and obtaining a report by the referring physician;
3. 3.Increasing the average number of radiology reports provided by a radiologist per shift.

Conditions

AI (Artificial Intelligence); Mammary Cancer; Lung Cancer; X-Rays; Lesion; COVID-19; Chest--Diseases; Abdomen Disease; Brain Disease; Fractures, Bone

Keywords

AI (Artificial Intelligence); Computed tomography; Low-dose CT; X-ray chest; mammography; COVID-19; Chest--Diseases; Abdomen Disease; Brain Disease; Fractures, Bone

Outcome Measures

Primary Outcomes (1)

• Number of errors

  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.

  Upon completion, up to 4 years

Secondary Outcomes (3)

• Report turnaround time

  Upon completion, up to 3 year

• Number of reports

  Upon completion, up to 4 years

• Change in the errors of services per the feedback form

  Upon completion, up to 4 years

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients over the age of 18 attending outpatient clinics

You may qualify if:

• 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

You may not qualify if:

• Another type of study (including a different modality and anatomical area)

Sponsors & Collaborators

• Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department: lead

Study Sites (1)

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Moscow, Russia

RECRUITING

Related Links

• Experiment on the use of innovative computer vision technologies for analysis of medical images in the Moscow healthcare system

MeSH Terms

Conditions

Breast Neoplasms; Lung Neoplasms; COVID-19; Brain Diseases; Fractures, Bone

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Diseases; Respiratory Tract Diseases; Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Central Nervous System Diseases; Nervous System Diseases; Wounds and Injuries

Study Officials

• Anton Vladzymyrskyy

  Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

  STUDY DIRECTOR

Central Study Contacts

Kirill Arzamasov, PhD

CONTACT

+79152514838; ArzamasovKM@zdrav.mos.ru

Olga Omelyanskaya

CONTACT

+79263948149; OmelyanskayaOV@zdrav.mos.ru

Study Design

• Study Type: observational
• Observational Model: CASE CROSSOVER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 17, 2020
• First Posted: July 28, 2020
• Study Start: February 21, 2020
• Primary Completion: January 1, 2024
• Study Completion: January 1, 2024
• Last Updated: May 26, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

RU (1)