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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition.

Farnaz Sedighin1, Maryam Monemian1, Zahra Zojaji2

  • 1Medical Image and Signal Processing Research Center, Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Medical Signals and Sensors
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI)-based computer-aided diagnosis (CAD) tools are advancing macular disease detection using optical coherence tomography (OCT) images. The Isfahan AI-2023 challenge evaluated AI classification methods, highlighting deep learning

Keywords:
Age-related macular degenerationIsfahan artificial intelligence challengechoroidal neovascularizationdiabetic macular edemamacular holeoptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Computer-aided diagnosis (CAD) systems, particularly those utilizing artificial intelligence (AI), are increasingly vital for diagnosing macular diseases.
  • AI-based CADs offer enhanced speed, objectivity, and thoroughness, serving as valuable assistance tools for clinicians.
  • Retinal AI-CADs specifically aid ophthalmologists in analyzing optical coherence tomography (OCT) images, improving diagnostic accuracy and accessibility.

Purpose of the Study:

  • To evaluate current AI-based classification methods for macular pathology detection through the Isfahan AI-2023 challenge.
  • To provide objective, formal evaluations of alternative AI tools for retinal diagnostics.
  • To identify the most successful algorithms for analyzing OCT images in macular disease diagnosis.

Main Methods:

  • A curated dataset of OCT images from normal subjects and patients with macular disorders (diabetic macular edema, etc.) was provided.
  • The dataset included labeled training and unlabeled test sets, accessible to challenge participants.
  • Algorithms were developed and tested by researchers aiming to maximize performance on the test set labels.

Main Results:

  • Multiple submissions demonstrated significant interest in AI-CAD technology for macular pathology.
  • Deep learning-based methods proved capable of learning critical features from pathological OCT images.
  • The competition facilitated the comparison and evaluation of various AI classification algorithms.

Conclusions:

  • The Isfahan AI-2023 challenge successfully evaluated AI-based classification methods for macular diseases.
  • Deep learning approaches show promise but require careful model selection for imbalanced datasets.
  • Further development in AI-CAD technology is crucial for advancing eye care, especially for underserved populations.