AI Classifies Multi-Retinal Diseases
Deep Learning-Based Automated Classification of Multi-Retinal Disease From Fundus Photography
1 other identifier
observational
10,000
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Nov 2020
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
October 13, 2020
CompletedFirst Posted
Study publicly available on registry
October 19, 2020
CompletedStudy Start
First participant enrolled
November 1, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
November 1, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
December 1, 2021
CompletedDecember 11, 2020
October 1, 2020
1 year
October 13, 2020
December 9, 2020
Conditions
Outcome Measures
Primary Outcomes (4)
Area under curve
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 area under curve to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.
1 week
Sensitivity and specificity
Taken the results of the expert panel as the gold standard, we will use sensitivity and specificity to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.
1 week
Positive and negative predictive value
Taken the results of the expert panel as the gold standard, we will use positive and negative predictive value to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.
1 week
Accuracy
Taken the results of the expert panel as the gold standard, we will use accuracy to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.
1 week
Study Arms (2)
Retinal multi-diseases diagnosed by DL algorithm
Retinal multi-diseases diagnosed by expert panel
Interventions
DL algorithm automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.
Expert panel classifies multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.
Eligibility Criteria
community derived dataset
You may qualify if:
- fundus photography around 45° field which covers optic disc and macula
- complete patient identification information;
You may not qualify if:
- incomplete patient identification information
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Wen-Bin Wei
Beijing, Beijing Municipality, 100730, China
Related Publications (1)
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.
PMID: 36878715DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
October 13, 2020
First Posted
October 19, 2020
Study Start
November 1, 2020
Primary Completion
November 1, 2021
Study Completion
December 1, 2021
Last Updated
December 11, 2020
Record last verified: 2020-10