Artificial Intelligence, Cataract
Conditions
Keywords
Cataract, Artificial Intelligence, Medical Referral Pattern
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.
Interventions
DEVICECataract AI agent
An artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts.
Sponsors
Xidian University
Sun Yat-sen University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic accuracy of the cataract AI agent | 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. |
Outcome results
None listed