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Journal of Medical Signals and Sensors، جلد ۱۵، شماره ۱، صفحات ۱۰-۴۱۰۳
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Age-related macular degeneration,choroidal neovascularization,diabetic macular edema,Isfahan artificial intelligence challenge,macular hole,optical coherence tomography
نویسندگان مقاله | Farnaz Sedighin
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


| Maryam Monemian
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Zahra Zojaji
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Ahmadreza Montazerolghaem
Department of Electrical Engineering, University of Isfahan, Isfahan, Iran


| Mohammad Amin Asadinia
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran


| Seyed Mojtaba Mirghaderi
Electrical Engineering Faculty, K. N. Toosi University, Tehran, Iran


| Seyed Amin Naji Esfahani
Department of Electrical Engineering, University of Isfahan, Isfahan, Iran


| Mohammad Kazemi
Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, Iran


| Reza Mokhtari
Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, Iran


| Maryam Mohammadi
Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, Iran


| Mohadese Ramezani
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


| Mahnoosh Tajmirriahi
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Hossein Rabbani


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/739
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Methodology Articles
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