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Related Experiment Video

Updated: May 11, 2026

Detecting Abnormalities in Choroidal Vasculature in a Mouse Model of Age-related Macular Degeneration by Time-course Indocyanine Green Angiography
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Deep Learning-Based Choroidal Boundary Detection in Geographic Atrophy Using Spectral-Domain Optical Coherence

Elham Rahmanipour1, Nasiq Hasan2, Adarsh Gadari2,3

  • 1Immunology Research Center, Mashhad University of Medical Sciences, Mashhad 91779 48564, Iran.

Diagnostics (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

A deep learning model significantly reduces time for choroidal boundary detection in geographic atrophy (GA) using spectral-domain OCT (SD-OCT). AI-assisted workflows cut manual effort by 90%, but human verification remains crucial for accuracy.

Keywords:
automated image analysischoroidal segmentationdeep learninggeographic atrophyswept-source OCT

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Geographic atrophy (GA) affects central vision, necessitating accurate monitoring.
  • Spectral-domain OCT (SD-OCT) is crucial for visualizing retinal layers, including the choroid.
  • Automated choroidal boundary detection aids in analyzing GA progression.

Purpose of the Study:

  • Evaluate deep learning model challenges for choroidal boundary detection in GA eyes.
  • Assess the workflow efficiency of an AI-assisted manual verification approach.

Main Methods:

  • Retrospective analysis of 5723 SD-OCT scans from GA patients.
  • Utilized NMI ChoroidAI for segmenting choroidal inner (CIB) and outer (COB) boundaries.
  • Compared AI-assisted workflow against manual segmentation for accuracy and time.

Main Results:

  • CIB detection showed high accuracy (94.8% accurate, F1 score 0.97).
  • COB detection was more prone to errors (19.0% deviation), but 94.2% were acceptable with minor deviations.
  • AI-assisted workflow reduced processing time by ~90% (7h manual vs. 45min AI+human).

Conclusions:

  • Deep learning model shows limitations in COB detection due to artifacts.
  • AI-assisted approach substantially reduces human effort in choroidal segmentation.
  • Mandatory human verification is essential to correct errors before clinical application.