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Deep learning in optical coherence tomography: Where are the gaps?

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  • 1College of Future Technology, Peking University, Beijing, China.

Clinical & Experimental Ophthalmology
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Deep learning (DL) shows promise for automating optical coherence tomography (OCT) image analysis in ophthalmology for tasks like segmentation and disease classification. However, challenges like data scarcity and lack of transparency must be addressed for clinical use.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Optical coherence tomography (OCT) is crucial for diagnosing eye diseases by imaging the macula and optic nerve head.
  • Interpreting OCT images requires specialized expertise due to potential artefacts and confounding conditions affecting quantitative measurements.
  • Deep learning (DL) methods are increasingly explored for automated OCT image analysis.

Purpose of the Study:

  • To review current trends in DL-based OCT image analysis in ophthalmology.
  • To identify existing gaps and challenges in the field.
  • To suggest future research directions for clinical application.

Main Methods:

  • Review of studies applying DL to analyze OCT images.
  • Identification of key tasks where DL shows performance.
  • Analysis of challenges hindering clinical adoption.

Main Results:

  • DL demonstrates promising performance in OCT image segmentation, quantification, disease classification, progression prediction, and triage.
  • Identified challenges include scarce public data, real-world performance discrepancies, model transparency issues, regulatory hurdles, and limited OCT accessibility.

Conclusions:

  • DL offers significant potential for enhancing OCT image analysis in ophthalmology.
  • Addressing data availability, model interpretability, and regulatory standards is critical for successful clinical integration.
  • Further research is needed to overcome current limitations before widespread clinical adoption.