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Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis
Marco A C Olyntho1, Carlos A C Jorge2, Everton B Castanha3
1D´Olhos Day Hospital, São José do Rio Preto-SP.
Journal of Glaucoma
|May 15, 2024
Summary
Deep learning algorithms (DLA) using anterior segment optical coherence tomography (AS-OCT) show high accuracy in detecting angle closure, comparable to traditional gonioscopy. This advanced technology offers a promising tool for glaucoma screening.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Angle closure glaucoma is a significant cause of vision loss.
- Accurate detection of angle closure is crucial for timely intervention.
- Traditional gonioscopy requires specialized skills and direct patient contact.
Approach:
- A systematic review and meta-analysis was conducted.
- Evaluated deep learning algorithms (DLA) applied to anterior segment optical coherence tomography (AS-OCT) images.
- Compared DLA performance against gonioscopy for diagnosing angle closure.
Key Points:
- The meta-analysis included 6 studies with 5269 patients.
- Deep learning algorithms demonstrated high diagnostic performance.
- Pooled sensitivity was 94.0% and pooled specificity was 93.6%.
Conclusions:
- Deep learning algorithms applied to AS-OCT show excellent accuracy for identifying angle closure.
- This technology can serve as a valuable screening tool, especially in underserved populations.
- DLA offers a non-contact, objective method for angle closure assessment.
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