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Validation of the Utility of Ophthalmology Intelligent Diagnostic System

NCT03499145 — Completed, 615 participants, Sun Yat-sen University
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Validation of the Utility of Ophthalmology Intelligent Diagnostic System: A Clinical Trial

RegistryNCT03499145
StatusCompleted
Study typeObservational
Enrollment615 (Actual)
SponsorSun Yat-sen University (Other); collaborators: Ministry of Health, China, Xidian University
ConditionsOphthalmopathy, Artificial Intelligence
InterventionsOphthalmology diagnostic system. (Device)
Datesstart 2018-04-01, primary completion 2019-08-31, completion 2019-08-31, first posted 2018-04-17, last updated 2019-10-21
Locations1 site in China

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-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.

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+Arms and interventions

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

+Outcome measures

Primary outcomes

  • The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system. [Up to 5 years]

+Eligibility

Sex: All, accepts healthy volunteers

Inclusion Criteria:

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

+Locations (1)

FacilityCityCountryStatus
Zhongshan Ophthalmic Center, Sun Yat-sen UniversityGuangzhou, GuangdongChina

+Publications

  • Results publication: 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 32572198

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