Artificial Intelligence, Ophthalmopathy
Conditions
Keywords
conjunctivitis, keratitis, pterygium, cataracts
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.
Interventions
DEVICEOphthalmology diagnostic system.
An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Sponsors
Ministry of Health, China
Xidian University
Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* Patients and residents who underwent ophthalmic examination of the eye and recorded their ocular information in the outpatient clinic and community.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system. | Up to 5 years |
Countries
China
Outcome results
None listed