Computer Aided Diagnosis of Multiple Eye Fundus Diseases From Color Fundus Photograph

NCT ID: NCT04723160

Last Updated: 2021-12-30

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 748 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-08-10
• Study Completion Date: 2021-05-30

Brief Summary

Blindness can be caused by many ocular diseases, such as diabetic retinopathy, retinal vein occlusion, age-related macular degeneration, pathologic myopia and glaucoma. Without timely diagnosis and adequate medical intervention, the visual impairment can become a great burden on individuals as well as the society. It is estimated that China has 110 million patients under the attack of diabetes, 180 million patients with hypertension, 120 million patients suffering from high myopia and 200 million people over 60 years old, which suggest a huge population at the risk of blindness. Despite of this crisis in public health, our society has no more than 3,000 ophthalmologists majoring in fundus oculi disease currently. As most of them assembling in metropolitan cities, health system in this field is frail in primary hospitals. Owing to this unreasonable distribution of medical resources, providing medical service to hundreds of millions of potential patients threatened with blindness is almost impossible.

To solve this problem, this software (MCS) was developed as a computer-aided diagnosis to help junior ophthalmologists to detect 13 major retina diseases from color fundus photographs. This study has been designed to validate the safety and efficiency of this device.

Detailed Description

As a prospective clinical trial, This study enjoys multicentric, blind film reading, self-control and superiority test design. In total, 1,500 retinal fundus images from 750 individuals in need of fundus examination (one image for every single eye) were selected. Then a test group, along with a control group was set up in our study. For the test group, ophthalmologists read images with the aid of the assistant software(MCS). In contrast, the same work in the control group was finished by ophthalmologists independently. Meanwhile, the gold standard were obtained from the cooperation of senior ophthalmologists. Diagnoses of both groups were compared with those of the gold standard, thus the investigators could evaluated the safety and effectiveness of this assistant software in diagnosis.

The primary endpoint of this study is the superiority of the consistency rate of the test group. A diagnosis for an image is consistent if it gives the same negative result as the reference standard, or reveals any one condition indicated by the reference standard. The consistency rate is the rate of consistent diagnoses for all the involved images. One control group is designed, where each doctor reads and diagnoses, and give at most 3 possible conditions for each image. In the test group, doctors do the same thing with the help of this software. The investigators in the test group and control group are the same and they are chosen from ophthalmologists with 1~3 years experience. The reference standard of each fundus image is collaboratively given by retinal specialists/fellows from 5 centers. The investigator of XieHe center is the arbitrator if full consensus cannot be reached for any image during the building of reference standard.

Conditions

Diabetic Retinopathy; Retinal Vein Occlusion; Retinal Artery Occlusion; Central Serous Chorioretinopathy; Pathologic Myopia; Retinitis Pigmentosa; Epiretinal Membrane; Macular Holes; Nonexudative Age-related Macular Degeneration; Exudative Age Related Macular Degeneration; Suspect Glaucoma; Optic Atrophy; Retinal Detachment

Keywords

multiple eye fundus diseases

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: PROSPECTIVE

Study Groups

Test Group

ophthalmologists read images applying the assistant software

Software assisted imaging diagnosis

Intervention Type: DIAGNOSTIC_TEST

In the test group, diagnoses are given with the help of the software.

Control Group

ophthalmologists read images independently

No interventions assigned to this group

Interventions

Software assisted imaging diagnosis

In the test group, diagnoses are given with the help of the software.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Age between 18 and 75.
• Anyone need to take fundus photograph in clinical.
• Understand the study and volunteer to sign the informed consent.
• For fundus images of participants, the optic disc, fovea, the upper and lower vessel bow should be included in the fundus field.

Exclusion Criteria

• Participants has any eye that cannot take fundus photos.
• Participants have joined or is participating in other clinical trial within one month.
• Participants who have any other issue that cannot be enrolled.
• Participants with cloudy refractive media that cannot take fundus photos or get clouding fundus photos.
• Participants with low quality fundus photos like incompetent vision field, overexposed/underexposed, out of focus, too many shadow or dirties and so on.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Peking University First Hospital

OTHER

Sponsor Role: collaborator

Beijing Municipal Science & Technology Commission

OTHER

Sponsor Role: collaborator

Visionary Intelligence Ltd.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

You xin Chen, PHD

Role: STUDY_CHAIR

Peking Union Medical College

Locations

Peking Union Medical College Hospital, Chinese Academy of Medical Sciences

Beijing, Beijing Municipality, China

The Second Hospital of Hebei Medical University

Shijiazhuang, Hebei, China

West China Hospital of Sichuan University

Chengdu, Sichuan, China

Tianjin Medical University Eye Hospital

Tianjin, Tianjin Municipality, China

Eye Hospital, WMU Zhejiang Eye Hospital

Wenzhou, Zhejiang, China

Countries

China

References

Li B, Chen H, Yu W, Zhang M, Lu F, Ma J, Hao Y, Li X, Hu B, Shen L, Mao J, He X, Wang H, Ding D, Li X, Chen Y. The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial. NPJ Digit Med. 2024 Jan 11;7(1):8. doi: 10.1038/s41746-023-00991-9.

Reference Type: DERIVED

PMID: 38212607 (View on PubMed)

Other Identifiers

BLG-20200101

Identifier Type: -

Identifier Source: org_study_id