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Validation of a Universal Cataract Intelligence Platform

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT03623971
Recruitment Status : Completed
First Posted : August 9, 2018
Last Update Posted : August 9, 2018
Xidian University
Information provided by (Responsible Party):
Haotian Lin, 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.

Condition or disease Intervention/treatment Phase
Cataract Artificial Intelligence Device: Cataract AI agent Not Applicable

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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 500 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Validation of the Utility of a Universal Cataract Intelligence Platform
Actual Study Start Date : January 1, 2013
Actual Primary Completion Date : June 1, 2017
Actual Study Completion Date : June 1, 2017

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Cataract

Arm Intervention/treatment
Experimental: Artificial Intelligence
A universal diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of cataract.
Device: Cataract AI agent
An artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts.

Primary Outcome Measures :
  1. Diagnostic accuracy of the cataract AI agent [ Time Frame: 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.

Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No

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.

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Responsible Party: Haotian Lin, Clinical Professor, Sun Yat-sen University Identifier: NCT03623971    
Other Study ID Numbers: CCPMOH2018- China7
First Posted: August 9, 2018    Key Record Dates
Last Update Posted: August 9, 2018
Last Verified: August 2018

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Haotian Lin, Sun Yat-sen University:
Artificial Intelligence
Medical Referral Pattern
Additional relevant MeSH terms:
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Lens Diseases
Eye Diseases