NCT03623971

Validation of a Universal Cataract Intelligence Platform

Validation of the Utility of a Universal Cataract Intelligence Platform

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 500 (actual)
• Sponsor: Sun Yat-sen University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

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.

Conditions

• Cataract
• Artificial Intelligence

Interventions

Type	Name	Description
DEVICE	Cataract AI agent	An artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts.

Timeline

• Start date: 2013-01-01
• Primary completion: 2017-06-01
• Completion: 2017-06-01
• First posted: 2018-08-09
• Last updated: 2018-08-09