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Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia.

So-Jin Park1,2, Taehoon Ko1,2, Chan-Kee Park3

  • 1Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.

Diagnostics (Basel, Switzerland)
|March 25, 2022
PubMed
Summary

A new deep learning algorithm can automatically diagnose pathologic myopia using 3D optical coherence tomography scans. This tool offers high accuracy, aiding in prompt diagnosis and preventing vision impairment.

Keywords:
convolutional neural networksdeep learningmyopiaoptical coherence tomographytransfer learning

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Pathologic myopia leads to vision impairment and blindness, necessitating early diagnosis.
  • Current diagnostic methods for pathologic myopia are subjective, time-consuming, and costly.
  • A need exists for automated, rapid diagnostic tools for pathologic myopia.

Purpose of the Study:

  • To develop and evaluate a deep learning algorithm for the automatic diagnosis of pathologic myopia.
  • To utilize 3D optical coherence tomography (OCT) volumetric images (C-scan) for automated detection.
  • To compare the performance of different convolutional neural network architectures.

Main Methods:

  • A deep learning model was trained using 3D OCT C-scan images from 367 eyes.
  • Transfer learning was applied using pre-trained models: ResNet18, ResNext50, EfficientNetB0, and EfficientNetB4.
  • Model performance was assessed using accuracy, sensitivity, specificity, and AUROC, with Grad-CAM for feature visualization.

Main Results:

  • The EfficientNetB4-based model achieved the highest performance.
  • Achieved 95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC in identifying pathologic myopia.
  • Demonstrated the potential of deep learning for objective and efficient diagnosis.

Conclusions:

  • An automated deep learning algorithm using 3D OCT images can effectively diagnose pathologic myopia.
  • The EfficientNetB4 model shows superior performance, offering a promising diagnostic tool.
  • This approach can facilitate quicker and more reliable diagnosis, potentially reducing vision loss.