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Automated OCT angiography image quality assessment using a deep learning algorithm.

J L Lauermann1, M Treder1, M Alnawaiseh1

  • 1Department of Ophthalmology, University of Muenster Medical Center, Domagkstrasse 15, 48149, Muenster, Germany.

Graefe'S Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie
|May 24, 2019
PubMed
Summary
This summary is machine-generated.

A deep learning algorithm (DLA) can automatically assess optical coherence tomography angiography (OCTA) image quality, achieving 90% accuracy. This technology shows promise for standardizing OCTA image quality evaluation.

Keywords:
Artificial intelligenceDeep learningImage analysisImage artifactsOptical coherence tomography angiographyRetina

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Optical coherence tomography angiography (OCTA) is a crucial imaging technique in ophthalmology.
  • Standardized assessment of OCTA image quality is essential for reliable diagnostic interpretation.
  • Current manual image quality assessment can be time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a deep learning algorithm (DLA) for automated image quality assessment in OCTA.
  • To expedite and standardize the evaluation of OCTA image quality.

Main Methods:

  • A deep convolutional neural network (DCNN) was trained and validated on 160 en-face macular OCTA scans.
  • Images were retrospectively classified as sufficient or insufficient quality based on artifact and segmentation scores.
  • The DLA was tested on 40 untrained OCTA images to evaluate its classification performance.

Main Results:

  • The DLA achieved high accuracy in classifying OCTA images, with 90% sensitivity, 90% specificity, and 90% overall accuracy.
  • The algorithm demonstrated strong performance with a validation accuracy of 100% and a training accuracy of 97%.
  • The DLA effectively discriminated between sufficient and insufficient image quality (p < 0.001).

Conclusions:

  • Deep learning offers a promising, automated approach for distinguishing between sufficient and insufficient OCTA image quality.
  • This DLA has the potential to contribute to the establishment of standardized image quality criteria for OCTA.
  • Automated quality assessment can enhance the efficiency and reliability of OCTA interpretation.