Validation of a Universal Cataract Intelligence Platform

Sun Yat-sen University

Status: Completed

Conditions: Cataract; Artificial Intelligence

Treatments: Device: Cataract AI agent

Study type: Interventional

Funder types: Other

Identifiers

NCT03623971

CCPMOH2018- China7

Details and patient eligibility

About

This study established and validated a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multi-level clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.The datasets were labeled using a three-step strategy: (1) categorize slit lamp photographs into four separate capture modes; (2) diagnose each photograph as a normal lens, cataract or a postoperative eye; and (3) based on etiology and severity, further classify each diagnosed photograph for a management strategy of referral or follow-up. A deep residual convolutional neural network (CS-ResCNN) was used for the image classification task. Moreover, we integrated the cataract AI agent with a real-world multi-level referral pattern involving self-monitoring at home, primary healthcare, and specialized hospital services.

Enrollment: 500 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the primary healthcare center.

Exclusion criteria

The patients who cannot cooperate with the examinations.

Trial design

Primary purpose: Diagnostic

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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500 participants in 1 patient group

Artificial Intelligence

Experimental group

Description:

A universal diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of cataract.

Treatment:

Device: Cataract AI agent