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Artificial Intelligence Applications in OCT and OCTA for Diabetic Retinopathy: A Systematic Review
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DOI：10.1016/j.exer.2026.111046.png)
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Abstract
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• Providing brief information about the current available public OCT and OCTA datasets and underscoring the critical role these publicly accessible datasets play in advancing both research and the development of novel algorithms and deep learning (DL) approaches. • Reporting more than 150 studies on segmentation and classification methods using both DL and Machine Learning (ML). • Giving the advantages and disadvantages of using DL methods in OCT image in compared to ML approach and reviewing the state of arts on these two subjects. • Identifying the key shortcomings in current research and directions for prospective investigation.
Keywords:
OCT
OCTA
Deep Learning
Machine Learning
Diabetic Retinopathy
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