Artificial Intelligence, Cataract
Conditions
Brief summary
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. The artificial intelligence for systematic clinical application has not yet been successfully validated. Currently, the main prevention strategy for rare diseases is to build specialized care centers. However, these centers are scattered, and their coverage is insufficient, resulting in inadequate health care among a large proportion of rare disease patients. Here, the investigators use deep learning to create CC-Cruiser, an intelligence agent involving three functional networks: pick-up networks for diagnostics, evaluation networks for risk stratification and strategist networks to provide assisted treatment decisions. The investigator also establish a cloud intelligence platform for multi-hospital collaboration and conduct clinical trial and website-based study to validate its versatility.
Interventions
DEVICECC-Cruiser
An artificial intelligence to make comprehensive evaluation and treatment decision of congenital cataracts
Sponsors
Ministry of Health, China
Xidian University
Sun Yat-sen University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
No minimum to 80 Years
Healthy volunteers
No
Inclusion criteria
* Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the collaborating hospital.
Exclusion criteria
\-
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The proportion of accurate, mistaken and miss detection of CC-Cruiser. | Up to 4 years |
Countries
China
Outcome results
None listed