Validation of a Universal Cataract Intelligence Platform
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|ClinicalTrials.gov Identifier: NCT03623971|
Recruitment Status : Completed
First Posted : August 9, 2018
Last Update Posted : August 9, 2018
Sun Yat-sen University
Information provided by (Responsible Party):
Haotian Lin, Sun Yat-sen University
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|
|Study Type :||Interventional (Clinical Trial)|
|Actual Enrollment :||500 participants|
|Intervention Model:||Single Group Assignment|
|Masking:||None (Open Label)|
|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|
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 :
- 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.
No Contacts or Locations Provided