Validation of a Universal Cataract Intelligence Platform

Cataract Clinical Trial

Official title:

Validation of the Utility of a Universal Cataract Intelligence Platform

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03623971
• Other study ID #: CCPMOH2018- China7
• Status: Completed
• Phase: N/A
• Start date: January 1, 2013
• Est. completion date: June 1, 2017

Study information

• Verified date: August 2018
• Source: Sun Yat-sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 500
• Est. completion date: June 1, 2017
• Est. primary completion date: June 1, 2017
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Cataract

Intervention

Device:
• Cataract AI agent
An artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts.

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
Sun Yat-sen University	Xidian University

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic accuracy of the cataract AI agent	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.	6 months