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Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition Publisher



Sedighin F1 ; Monemian M1 ; Zojaji Z2 ; Montazerolghaem A2 ; Asadinia MA3 ; Mirghaderi SM4 ; Esfahani SAN5 ; Kazemi M3 ; Mokhtari R6 ; Mohammadi M6 ; Ramezani M6 ; Tajmirriahi M1 ; Rabbani H1
Authors
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Authors Affiliations
  1. 1. Medical 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
  2. 2. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
  3. 3. Department of Electrical Engineering, University of Isfahan, Isfahan, Iran
  4. 4. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
  5. 5. Electrical Engineering Faculty, K. N. Toosi University, Tehran, Iran
  6. 6. Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, Iran

Source: Journal of Medical Signals and Sensors Published:2025


Abstract

Background: Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison. Methods: Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AI-CAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms. Results: A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results. Conclusions: The competition is organized to evaluate the current AI-based classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets. © 2025 Journal of Medical Signals & Sensors.
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