Validation of the Utility of Rare Disease Intelligence Platform

Sun Yat-sen University

Status: Completed

Conditions: Cataract; Artificial Intelligence

Treatments: Device: CC-Cruiser

Study type: Interventional

Funder types: Other

Identifiers

NCT02748044

CCPMOH2016-China3

Details and patient eligibility

About

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.

Enrollment: 53 patients

Sex: All

Ages: Under 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the collaborating hospital.

Exclusion criteria

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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53 participants in 1 patient group

Eligible patients for CC-Cruiser test

Experimental group

Treatment:

Device: CC-Cruiser