Explainable Ocular Fundus Diseases Report Generation System
Explainable Multimodal Deep Neural Networks for Identifying Ocular Fundus Diseases and Report Generation
1 other identifier
observational
15,000
1 country
1
Brief Summary
To establish a deep learning system of various ocular fundus disease analytics based on the results of multimodal examination images. The system can analyze multimodal ocular fundus images, make diagnoses and generate corresponding reports.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2011
Longer than P75 for all trials
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
Study Start
First participant enrolled
January 1, 2011
CompletedFirst Submitted
Initial submission to the registry
November 15, 2022
CompletedFirst Posted
Study publicly available on registry
November 18, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 1, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
July 1, 2024
CompletedJuly 11, 2023
July 1, 2023
12.9 years
November 15, 2022
July 10, 2023
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Area under the receiver operating characteristic curve of the deep learning system
The investigators will calculate the area under the receiver operating characteristic curve of the deep learning system and compare this index with human ophthalmologists.
Baseline
Secondary Outcomes (2)
Intersection-Over-Union of the models' explanation accuracy
Baseline
Sensitivity and Specificity of the deep learning system
Baseline
Study Arms (3)
Training set
Multimodal ocular fundus images and corresponding reports collected from multiple screening sites in China.
Internal Validation set
Records separated from the training set.
External Test set
Multimodal ocular fundus images and corresponding reports collected from multi-centers in China and around the world.
Interventions
Through various modalities of ocular fundus imaging, combining with clinical data and the experience of clinicians to diagnose different fundus diseases.
Eligibility Criteria
Ocular fundus images were collected from different health care institutes all over China and from other countries.
You may qualify if:
- The quality of multimodal ocular fundus disease examination images and corresponding reports should be clinically acceptable.
You may not qualify if:
- Reports with key information missing.
- Images with severe image resolution reductions, blur or artifacts were excluded from further analysis.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Zhognshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510000, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Yingfeng Zheng, M.D. Ph.D
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity,Guangzhou, Guangdong, China, 510060
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- OTHER
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- M.D, Ph.D
Study Record Dates
First Submitted
November 15, 2022
First Posted
November 18, 2022
Study Start
January 1, 2011
Primary Completion
December 1, 2023
Study Completion
July 1, 2024
Last Updated
July 11, 2023
Record last verified: 2023-07
Data Sharing
- IPD Sharing
- Will not share