CompletedNCT03499145Updated Oct 21, 2019

Validation of the Utility of Ophthalmology Intelligent Diagnostic System

An observational study in Ophthalmopathy and Artificial Intelligence, sponsored by Sun Yat-sen University. Completed at 1 site in China. Per ClinicalTrials.gov, last updated 2019-10-21.

Sponsored by Sun Yat-sen University · Observational

Study type: Observational
Model: Cohort
Time perspective: Prospective
Enrollment: 615
Sex: All

Study 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.

Conditions studied

Ophthalmopathy
Artificial Intelligence

Browse trials for

Eye Diseases 741

Keywords

conjunctivitis
keratitis
pterygium
cataracts

Who can participate

Ages eligible: Child (0–17), Adult (18–64), Older adult (65+)
Sexes eligible: All
Accepts healthy volunteers: Yes
Sampling method: Probability sample

Study population

A prospective study of patients and residents who use the web platform for diagnosis.

Eligibility criteria

Inclusion Criteria:

Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.

Study design

Observational model: Cohort
Time perspective: Prospective
Enrollment: 615 participants (actual)
Patient registry: No

Groups and cohorts

Eligible patients for AI test.
Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Device: Ophthalmology diagnostic system.

Interventions

DeviceOphthalmology diagnostic system.
An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.

What researchers measure

Primary outcomes

The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system.
Time frame: Up to 5 years

Study locations

1 site

Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong 510000, China

References and documents

Publications

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. PubMed 32572198 ↗

Registry details

Key details

Study ID: NCT03499145
Lead sponsor: Sun Yat-sen University
Collaborators: Ministry of Health, China, Xidian University
Responsible party: Haotian Lin (Clinical Professor, Sun Yat-sen University) — Principal investigator
First posted: Apr 17, 2018
Start date: Apr 1, 2018
Primary completion: Aug 31, 2019
Completion: Aug 31, 2019
Last update: Oct 21, 2019

Oversight

Data monitoring committee: Yes
FDA-regulated drug: No
FDA-regulated device: No

View the source record on ClinicalTrials.gov ↗

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This study is completed, as verified in Oct 2019. You cannot join it, but the record below documents what was studied.

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