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Related Experiment Video

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Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
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iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence

Kai Jin1, Yan Yan1, Shuai Wang2

  • 1Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou 310009, China.

Journal of Clinical Medicine
|January 21, 2023
PubMed
Summary

A new deep learning system, iERM, accurately grades epiretinal membranes (ERM) using optical coherence tomography (OCT) scans. This interpretable AI tool aids in clinical diagnosis and treatment decisions for ERM.

Keywords:
deep learningepiretinal membranemulti-centeroptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Epiretinal membranes (ERM) are prevalent in individuals over 50.
  • Assessing ERM severity from optical coherence tomography (OCT) images is challenging due to limited reliable analysis methods.

Purpose of the Study:

  • To develop an interpretable, two-stage deep learning (DL) system named iERM for automatic ERM grading.
  • To enhance the accuracy and clinical utility of ERM assessment using OCT imaging.

Main Methods:

  • Trained the iERM system using human segmentation of key features for improved classification and interpretability.
  • Utilized a dataset of 4547 OCT B-scans from four OCT devices across nine international medical centers.

Main Results:

  • The iERM system demonstrated improved grading performance (1-5.9%) compared to traditional DL models.
  • Achieved high accuracy rates of 82.9% (internal) and 87.0%, 79.4% (external), comparable to retinal specialists.

Conclusions:

  • The iERM system offers a benchmark for improving DL model performance and interpretability in ERM grading.
  • This AI-driven approach shows potential for precise guidance in ERM diagnosis and treatment planning.