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
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Sponsor:
Sun Yat-sen University
Collaborator:
Xidian University
Information provided by (Responsible Party):
Haotian Lin, Sun Yat-sen University
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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 |
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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) |
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 |
Arm | Intervention/treatment |
---|---|
Experimental: Artificial Intelligence
A universal diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of cataract.
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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.
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Ages Eligible for Study: | Child, Adult, Older Adult |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | No |
Criteria
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.
No Contacts or Locations Provided
Responsible Party: | Haotian Lin, Clinical Professor, Sun Yat-sen University |
ClinicalTrials.gov Identifier: | NCT03623971 History of Changes |
Other Study ID Numbers: |
CCPMOH2018- China7 |
First Posted: | August 9, 2018 Key Record Dates |
Last Update Posted: | August 9, 2018 |
Last Verified: | August 2018 |
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:
Cataract Artificial Intelligence Medical Referral Pattern |
Additional relevant MeSH terms:
Cataract Lens Diseases Eye Diseases |