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AI Classifies Multi-Retinal Diseases

NCT ID: NCT04592068

Last Updated: 2020-12-11

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

10000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-11-01

Study Completion Date

2021-12-01

Brief Summary

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The objective of this study is to establish deep learning (DL) algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. The effectiveness and accuracy of the established algorithm will be evaluated in community derived dataset.

Detailed Description

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Retinal diseases seriously threaten vision and quality of life, but they often develop insidiously. To date, deep learning (DL) algorithms have shown high prospects in biomedical science, particularly in the diagnosis of ocular diseases, such as diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, glaucoma, and papilledema. However, there is still a lack of a single algorithm that can classify multi-diseases from fundus photography.

This cross-sectional study will establish a DL algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. We will use the receiver operating characteristic (ROC) curve to examine the ability of recognition and classification of diseases. Taken the results of the expert panel as the gold standard, we will use the evaluation indexes, such as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, etc, to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

Conditions

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Deep Learning Retinal Diseases

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Retinal multi-diseases diagnosed by DL algorithm

Retinal multi-diseases diagnosed by DL algorithm

Intervention Type DEVICE

DL algorithm automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.

Retinal multi-diseases diagnosed by expert panel

Retinal multi-diseases diagnosed by expert panel

Intervention Type OTHER

Expert panel classifies multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.

Interventions

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Retinal multi-diseases diagnosed by DL algorithm

DL algorithm automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.

Intervention Type DEVICE

Retinal multi-diseases diagnosed by expert panel

Expert panel classifies multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* fundus photography around 45° field which covers optic disc and macula
* complete patient identification information;

Exclusion Criteria

* incomplete patient identification information
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Beijing Tulip Partner Technology Co., Ltd, China

UNKNOWN

Sponsor Role collaborator

Beijing Tongren Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Wen-Bin Wei

Beijing, Beijing Municipality, China

Site Status RECRUITING

Countries

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China

Facility Contacts

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Wen-Bin Wei, MD

Role: primary

References

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Gu C, Wang Y, Jiang Y, Xu F, Wang S, Liu R, Yuan W, Abudureyimu N, Wang Y, Lu Y, Li X, Wu T, Dong L, Chen Y, Wang B, Zhang Y, Wei WB, Qiu Q, Zheng Z, Liu D, Chen J. Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases. Br J Ophthalmol. 2024 Feb 21;108(3):424-431. doi: 10.1136/bjo-2022-322940.

Reference Type DERIVED
PMID: 36878715 (View on PubMed)

Other Identifiers

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Retinal multi diseases

Identifier Type: -

Identifier Source: org_study_id