Validation of the Utility of Ophthalmology Intelligent Diagnostic System
1 other identifier
observational
615
1 country
1
Brief Summary
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical scenarios: 1) mass screening, 2) comprehensive clinical triage, 3) hyperfine diagnostic assessment, and 4) multi-path treatment planning. The investigator also establish a telemedicine system and conduct clinical trial and website-based study to validate its versatility.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Apr 2018
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
April 1, 2018
CompletedFirst Submitted
Initial submission to the registry
April 11, 2018
CompletedFirst Posted
Study publicly available on registry
April 17, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
August 31, 2019
CompletedStudy Completion
Last participant's last visit for all outcomes
August 31, 2019
CompletedOctober 21, 2019
October 1, 2019
1.4 years
April 11, 2018
October 17, 2019
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system.
Up to 5 years
Study Arms (1)
Eligible patients for AI test.
Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Interventions
An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Eligibility Criteria
A prospective study of patients and residents who use the web platform for diagnosis.
You may qualify if:
- Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Sun Yat-sen Universitylead
- Ministry of Health, Chinacollaborator
- Xidian Universitycollaborator
Study Sites (1)
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510000, China
Related Publications (1)
Li W, Yang Y, Zhang K, Long E, He L, Zhang L, Zhu Y, Chen C, Liu Z, Wu X, Yun D, Lv J, Liu Y, Liu X, Lin H. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders. Nat Biomed Eng. 2020 Aug;4(8):767-777. doi: 10.1038/s41551-020-0577-y. Epub 2020 Jun 22.
PMID: 32572198DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Clinical Professor
Study Record Dates
First Submitted
April 11, 2018
First Posted
April 17, 2018
Study Start
April 1, 2018
Primary Completion
August 31, 2019
Study Completion
August 31, 2019
Last Updated
October 21, 2019
Record last verified: 2019-10