Validation of a Universal Cataract Intelligence Platform

Not Applicable

Completed

• Conditions: Artificial Intelligence; Cataract
• Interventions: Device: Cataract AI agent

• Registration Number: NCT03623971
• Lead Sponsor: Sun Yat-sen University

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2013-01-01
• Primary Completion: 2017-06-01
• Study Completion: 2017-06-01
• Status Verified: 2018-08
• Study First Submitted: 2018-08-07
• Study First Submitted QC: 2018-08-07
• Last Update Submitted: 2018-08-07
• Study First Posted: 2018-08-09
• Last Update Posted: 2018-08-09

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 500

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.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Artificial Intelligence	Cataract AI agent	A universal diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of cataract.

Primary Outcome Measures

Name	Time	Method
Diagnostic accuracy of the cataract AI agent	6 months	AUC: area under the receiver operating curve; accuracy (ACC) = (TP + TN) / (TP + TN + FP + FN); sensitivity (SEN) = TP / (TP + FN); specificity (SPE) = TN / (TN + FP); TP = true positive; TN = true negative; FP = false positive; FN = false negative.