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Related Experiment Video

Updated: Jan 13, 2026

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography
07:44

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Published on: July 24, 2020

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Low-Cost and Fast Epiretinal Membrane Detection and Quantification Based on SD-OCT.

Seungju Baek1, Insup Lee2, Kuk Jin Jang3

  • 1Department of AI Convergence Engineering, Gyeongsang National University, Jinju 52828, South Korea.

IEEE Access : Practical Innovations, Open Solutions
|January 8, 2026
PubMed
Summary

A new, cost-effective method uses Spectral-Domain Optical Coherence Tomography (SD-OCT) B-scans to detect and quantify epiretinal membranes (ERM). This approach enhances visualization and analysis, aligning with clinical judgment for potential widespread application.

Keywords:
Medical AIYOLOdisease quantificationepiretinal membraneobject detectionretinal thicknessspectral domain OCT

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Epiretinal membrane (ERM) causes visual impairment due to retinal traction.
  • Spectral-Domain Optical Coherence Tomography (SD-OCT) is standard for ERM diagnosis.
  • Advanced Swept-Source OCT (SS-OCT) offers benefits but faces adoption barriers.

Purpose of the Study:

  • To develop a cost-effective pipeline for ERM detection and quantification using SD-OCT B-scans.
  • To introduce an Epiretinal Projection Image (EPI) for intuitive ERM visualization.
  • To enable objective ERM area quantification and spatial analysis.

Main Methods:

  • A novel Epiretinal Projection Image (EPI) technique was developed.
  • A YOLOv11x deep learning model was employed for ERM detection on SD-OCT B-scans.
  • An association scoring mechanism correlated EPI projections with retinal thickness maps.

Main Results:

  • High-precision ERM detection achieved (mAP@50: 0.882).
  • Accurate ERM area quantification via EPI projection.
  • Strong spatial correlation (0.771) found between ERM and retinal thickening.
  • Excellent clinical reliability and expert acceptability (ICC=0.94, κ=0.89, AR=93.3%).

Conclusions:

  • The proposed system accurately detects and quantifies ERM using accessible SD-OCT devices.
  • The EPI technique provides interpretable visualization and analysis.
  • The method shows potential for reliable clinical application in ERM assessment.