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Artificial intelligence in glaucoma: posterior segment optical coherence tomography.
Alfredo Gutierrez1,2, Teresa C Chen2,3
1Tufts School of Medicine.
Current Opinion in Ophthalmology
|February 2, 2023
Summary
Deep learning (DL) models show high accuracy in detecting glaucoma using optical coherence tomography (OCT) scans. While promising for glaucoma management, no DL tools are yet FDA-approved for diagnosis due to varied OCT definitions.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Glaucoma detection and surveillance rely heavily on optical coherence tomography (OCT) imaging.
- Deep learning (DL) presents a novel approach to analyze complex OCT data for diagnosing glaucomatous optic neuropathy (GON).
Approach:
- This review summarizes recent literature on DL model applications for glaucoma detection and surveillance using posterior segment OCT.
- DL models utilize OCT-derived parameters such as retinal nerve fiber layer (RNFL), macular scans, and optic nerve head (ONH) scans, often in combination.
Key Points:
- DL models achieve high diagnostic accuracy for GON, frequently employing combined OCT parameters for a comprehensive analysis.
- While RNFL segmentation is common, DL models increasingly integrate multiple OCT parameters.
- DL models for predicting glaucoma progression are less studied but are under development.
Conclusions:
- DL models offer efficient and objective tools for glaucoma management.
- Despite AI's commercial success in other ophthalmic diseases, a DL tool for glaucoma diagnosis lacks FDA approval, potentially due to the absence of a universal OCT-based definition of glaucoma.
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