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Related Experiment Video

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Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography.

Tyler Hyungtaek Rim1,2, Aaron Yuntai Lee3, Daniel S Ting1,2

  • 1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.

The British Journal of Ophthalmology
|April 20, 2021
PubMed
Summary

A new deep learning algorithm can accurately detect outer retinal layer (ORL) abnormalities in optical coherence tomography (OCT) scans. This computer-aided detection (CADe) system aids in differentiating normal from diseased retinas, potentially assisting physicians in diagnosis.

Keywords:
epidemiologyimagingretinatelemedicine

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Outer retinal layers (ORL) are crucial for retinal health.
  • Abnormalities in ORL are associated with conditions like choroidal neovascularisation (CNV) and retinitis pigmentosa (RP).
  • Accurate detection of ORL abnormalities is vital for timely diagnosis and treatment.

Purpose of the Study:

  • To develop a computer-aided detection (CADe) system for identifying outer retinal layer (ORL) abnormalities using optical coherence tomography (OCT).
  • To segment and classify normal versus abnormal ORL structures in OCT images.
  • To evaluate the diagnostic performance of the developed CADe system.

Main Methods:

  • A retrospective study included healthy individuals and patients with CNV or RP.
  • A deep learning (DL) algorithm was developed for automatic segmentation of three ORL.
  • A binary classifier was trained on segmented images to differentiate normal from abnormal ORL.

Main Results:

  • The DL-based CADe achieved high diagnostic accuracy with an Area Under the Curve (AUC) of 0.984 on an internal test set.
  • External validation demonstrated strong performance with AUCs of 0.957 and 0.978 on two independent datasets.
  • The system effectively highlighted normal ORL and omitted abnormal areas in CNV and RP cases.

Conclusions:

  • The developed CADe system accurately segments ORL and differentiates between normal and abnormal retinal structures from OCT images.
  • The CADe system's classification performance shows potential for aiding physicians in diagnosis.
  • This technology may offer future clinical applications for retinal disease management.