Neoplasms

Skin and Connective Tissue Diseases

All Conditions

Breast Neoplasms

Breast Diseases

Skin Diseases

Two radiologists interpret at least three views of each breast without AI independently.

Conventional-reading

One radiologist reads AB US images without AI first, then combines the indications of AI marks to make the final decision.

Second-reading

One radiologist identifies CAD marks first, then quickly browses the entire AB US examination to make the final decision.

Concurrent-reading

Two radiologists interpret only two views of each breast without AI independently.

Tow views-reading

One radiologist screens the breast cancer using Handheld US and interprets images immediately.

Handheld US-screening

Hongping Song, PHD,MD

A multi-center, prospective, cohort study to evaluate the efficiency of breast cancer screening based on Automated Breast Ultrasound (AB US) with remote reading mode.

The burden of breast cancer incidence and mortality is rapidly growing during the past two decades in China. Screening has been proven to be effective in detecting early-stage disease and reducing mortality of 10% to 39% due to breast cancer. US is used as a primary screening method among Chinese women because Asian women characteristically have higher-density breasts than other ethnic groups. However, US is dependent on operator experience, lack of standardized scanning protocols, limited ultrasound physician and heavy workloads. In order to improve the efficiency and quality of breast cancer screening among Chinese women, a new remote screening pattern based on AB US was proposed and studied for breast cancer in China. This multi-center, prospective, cohort study aims to evaluate the efficiency of breast cancer screening based on AB US with remote reading mode. In addition, we will also explore the practice value of AI on AB US screening and efficient image acquiring and reading modes of AB US.

Remote Breast Cancer Screening Study

Remote Breast Cancer Screening Based on Automated Breast Ultrasound Among Chinese Women: a Multi-center, Prospective, Cohort Study

The performance of breast cancer screening based on AB US with remote reading mode will be evaluated by cancer detection rate, recall rate, sensitivity, specificity, positive predictive value, early detection of breast cancer, interval cancers and time for image acquisition and interpretation.

Evaluate the performance of AI on breast cancer screening based on AB US with remote reading mode (Conventional-reading vs. Second-reading / Concurrent-reading)

Evaluate the performance of the image acquisition and interpretation modes in breast cancer screening based on AB US with remote reading mode (two views vs. three views)

Evaluate the performance of breast cancer screening according to the breast US device (Hand-held US vs. AB US)

The First Affiliated Hospital of the Fourth Military Medical University

Principal Investigator

Digestive System Diseases

Colorectal Neoplasms

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

Olympus Endoscopy Computer-Aided Detection (CADe) system

Standard of Care Endoscopy (HD Whitelight)

OIP-1

Karolin Boecker

This is a prospective, multicenter, randomized Trial to assess the safety and efficacy of the Olympus Endoscopy Computer-Aided Detection (CADe) system, OIP-1, in the detection of colorectal neoplasia's during real-time colonoscopy. The aim of this system is to improve physician performance in the detection of potential mucosal abnormalities during colonoscopy performed for primary Colorectal Cancer screening or postpolypectomy surveillance.

This Study aims to provide insight into whether the application of Artificial Intelligence (Olympus Endoscopy Computer-Aided Detection (CADe) system) increases the detection rate of adenomatous polyps in screening and / or surveillance colonoscopy procedures.

Safety and Efficacy of the Olympus CADe System in Real-time Colonoscopy

Safety and Efficacy of the Olympus Endoscopy Computer-Aided Detection (CADe) System in Detection of Colorectal Neoplasia's During Real-time Colonoscopy: A European Prospective, Multicenter, Randomized Controlled Trial (EuroCADe)

Device-, or procedure-related AEs and Serious AEs (SAEs) reported as incidence (%) and frequency (n).

Number of patients with no histological confirmation of adenoma, advanced adenoma, SSL, large hyperplastic polyp or carcinoma in any excised lesion divided by the number of colonoscopies where at least one polyp was resected

Proportion of patients with at least one histologically confirmed adenoma (including advanced adenomas) or carcinoma in the CADe assisted colonoscopy (CADe+ arm) versus conventional colonoscopy without CAD (CADe- arm).

Number of histologically confirmed polyps (i.e. adenomas, SSLs, or large (\>10mm) hyperplastic polyps divided by the total number of resections.

Number of histologically proven adenomas (including advanced adenomas) or carcinomas divided by the total number of colonoscopies

Time from procedure start (insertion of the colonoscope) until colon inspection during a typical withdrawal from cecum to rectosigmoid including any time needed for interventional procedures (biopsies, resection).

Time from deepest point of insertion (cecum) needed for complete inspection of the colon, excluding time spent on therapeutic intervention.

Cromsource

Olympus Europe SE & Co. KG

Respiratory Tract (Lung and Bronchial) Diseases

Infections

Wounds and Injuries

Nervous System Diseases

Symptoms and General Pathology

Lung Neoplasms

COVID-19

Fractures, Bone

Brain Diseases

Pneumonia

Pneumonia, Viral

Communicable Diseases

Respiratory Tract Infections

Virus Diseases

Coronavirus Infections

Coronaviridae Infections

Nidovirales Infections

RNA Virus Infections

Lung Diseases

Respiratory Tract Diseases

Central Nervous System Diseases

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.

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.

Kirill Arzamasov, PhD

Olga Omelyanskaya

Anton Vladzymyrskyy

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).

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

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.

Change of at least 30% of the time from the study completion to report finalization by a radiologist.

Change of at least 30% in the number of radiology reports provided by a radiologist per shift.

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)

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Remote Breast Cancer Screening Study	NCT04527510	Breast Cancer Mass Screening Cancer Screenin... Ultrasound Imag... AI (Artificial ...	Conventional-re... Second-reading Concurrent-read... Tow views-readi... Handheld US-scr...	35 Years -	The First Affiliated Hospital of the Fourth Military Medical University
Remote Breast Cancer Screening Study	NCT04527510	Breast Cancer Mass Screening Cancer Screenin... Ultrasound Imag... AI (Artificial ...	Conventional-re... Second-reading Concurrent-read... Tow views-readi... Handheld US-scr...	35 Years -	The First Affiliated Hospital of the Fourth Military Medical University
Safety and Efficacy of the Olympus CADe System in Real-time Colonoscopy	NCT05943288	Colorectal Canc...	OIP-1	45 Years - 80 Years	Olympus Europe SE & Co. KG
Experiment on the Use of Innovative Computer Vision Technologies for Analysis of Medical Images in the Moscow Healthcare System	NCT04489992	AI (Artificial ... Mammary Cancer Lung Cancer X-Rays; Lesion COVID-19 Chest--Diseases Abdomen Disease Brain Disease Fractures, Bone		18 Years -	Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

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