Validation of the Utility of Ophthalmology Intelligent Diagnostic System

Completed

• Conditions: Ophthalmopathy; Artificial Intelligence

• Registration Number: NCT03499145
• Lead Sponsor: Sun Yat-sen University

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2018-04-01
• Study First Submitted: 2018-04-11
• Study First Submitted QC: 2018-04-11
• Study First Posted: 2018-04-17
• Primary Completion: 2019-08-31
• Study Completion: 2019-08-31
• Status Verified: 2019-10
• Last Update Submitted: 2019-10-17
• Last Update Posted: 2019-10-21

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 615

Inclusion Criteria

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

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

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

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China