Validation of a Universal Cataract Intelligence Platform
August 7, 2018 updated by: Haotian Lin, Sun Yat-sen University
Validation of the Utility of a Universal Cataract Intelligence Platform
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.
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Study Type
Interventional
Enrollment (Actual)
500
Phase
- Not Applicable
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Description
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 Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: Artificial Intelligence
A universal diagnostic system.
An artificial intelligence to make comprehensive evaluation and treatment decision of cataract.
|
An artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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.
|
6 months
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Collaborators
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
January 1, 2013
Primary Completion (Actual)
June 1, 2017
Study Completion (Actual)
June 1, 2017
Study Registration Dates
First Submitted
August 7, 2018
First Submitted That Met QC Criteria
August 7, 2018
First Posted (Actual)
August 9, 2018
Study Record Updates
Last Update Posted (Actual)
August 9, 2018
Last Update Submitted That Met QC Criteria
August 7, 2018
Last Verified
August 1, 2018
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- CCPMOH2018- China7
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
product manufactured in and exported from the U.S.
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.