EFFECTIVENESS OF AUTOMATED DIAGNOSTIC SYSTEMS IN DETECTING EYE DISEASES BASED ON MEDICAL IMAGING
Keywords:
eye diseases, medical imaging, artificial intelligence, deep learning, convolutional neural network, automated diagnosis, retinopathy, ophthalmology.Abstract
This article examines the scientific and practical foundations of using automated diagnostic systems for detecting eye diseases through medical imaging. The effectiveness, accuracy, advantages, and application areas of artificial intelligence (AI) and deep learning algorithms in ophthalmology are analyzed, along with existing challenges. The research results demonstrate that automated systems enable early detection of ophthalmic diseases, accelerate diagnosis, and significantly reduce human error.
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References
Gulshan V. et al. “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.” JAMA, 2016.
De Fauw J. et al. “Clinically applicable deep learning for diagnosis and referral in retinal disease.” Nature Medicine, 2018.
Ting D. S. W. et al. “AI and Deep Learning in Ophthalmology.” British Journal of Ophthalmology, 2019.
Litjens G. et al. “A survey on deep learning in medical image analysis.” Medical Image Analysis, 2017.
Ministry of Health of the Republic of Uzbekistan. “Concept of Digital Medicine 2023–2025.” Tashkent, 2023.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
