Deep Learning-Based Optical Coherence Tomography and Optical Coherence Tomography Angiography Image Analysis: An...

Area of Science:

• Ophthalmology
• Artificial Intelligence
• Medical Imaging

Background:

• Deep learning (DL), a subset of artificial intelligence, excels in image classification and pattern recognition.
• DL is increasingly applied to analyze optical coherence tomography (OCT) and OCT angiography (OCTA) images in ophthalmology.
• Existing studies demonstrate DL's potential in disease detection, prognosis prediction, and quality control for OCT/OCTA images.

Purpose of the Study:

• To review recent studies on DL-based image analysis for OCT and OCTA.
• To discuss challenges and future directions for clinical deployment of DL in ophthalmology.

Main Methods:

• Review of recent literature on DL applications in OCT and OCTA image analysis.
• Identification and discussion of key challenges hindering clinical implementation.

Main Results:

• DL algorithms show good performance in disease detection, prognosis prediction, and image quality control using OCT and OCTA data.
• DL has the potential to improve diagnostic accuracy and clinical workflow efficiency.

Conclusions:

• DL holds significant promise for enhancing ophthalmic diagnostics using OCT and OCTA imaging.
• Addressing challenges like small datasets, standardization, robustness, interpretability, and validation is crucial for real-time clinical application.