Abstract
Justification and purpose of the study. The high social significance of diabetic retinopathy (DR), the complexity of early diagnosis and monitoring of the disease determine the urgency of developing a system for diagnosing diabetic changes in the fundus using artificial intelligence (AI) methods. The aim of the work is to build a demo prototype of a WEB service for recognizing signs of DR from fundus images using machine learning methods using the Python language and the Django framework.Materials and methods. The study used the Messidor dataset (1200 eyes), which is publicly available on the Internet and includes photos of healthy fundus (546 eyes) and fundus with pathology (654 eyes). With the help of the augmentation method, this set for the study is increased several times. The fundus image recognition system is based on the trained neural network ResNet50. The web service with a connected neural network model was developed on the Django framework.Results. The main result of the research is the development of a test prototype of a service for the diagnosis of diabetic fundus changes based on photos using machine learning tools, demonstrating the great potential of using AI to improve the effectiveness of decisions. The sensitivity of the neural network model during the diagnosis of DR, even on a small test sample and limited training time, was 85 %.Conclusion. The high efficiency and potential of AI methods in the construction of a system for automatic detection of fundus pathology in the framework of the developed in the Helmholtz National Medical Research Center of Eye Diseases automated medical decision-making system. In the future, this service can be used to improve the effectiveness of early diagnosis and monitoring of diabetic changes in the fundus in conditions of reduced availability of primary ophthalmological care in parts of the Russian Federation, including at the pre-medical stage.
Highlights
The high social significance of diabetic retinopathy (DR), the complexity of early diagnosis and monitoring of the disease determine the urgency of developing a system for diagnosing diabetic changes in the fundus using artificial intelligence (AI) methods
The web service with a connected neural network model was developed on the Django framework
The main result of the research is the development of a test prototype of a service for the diagnosis of diabetic fundus changes based on photos using machine learning tools, demonstrating the great potential of using AI to improve the effectiveness of decisions
Summary
Цель работы – построение демонстрационного прототипа WEB-сервиса для распознавания признаков ДР по снимкам глазного дна при помощи методов машинного обучения с использованием языка Python и фреймворка Django. Основным результатом исследования является разработка тестового прототипа сервиса для диагностики диабетических изменений глазного дна по фото с помощью инструментов машинного обучения, демонстрирующего большой потенциал применения ИИ для повышения эффективности принимаемых решений. Данный сервис в перспективе может быть использован с целью повышения эффективности ранней диагностики и мониторинга диабетических изменений глазного дна в условиях сниженной доступности первичной офтальмологической помощи на части территорий Российской Федерации, в том числе на доврачебном этапе. Ключевые слова: диабетическая ретинопатия; искусственный интеллект; сахарный диабет; диагностика; сервис; скрининг Для цитирования: Нероев В.В., Брагин А.А., Зайцева О.В. Разработка прототипа сервиса для диагностики диабетической ретинопатии по снимкам глазного дна с использованием методов искусственного интеллекта. Yevdokimov Moscow State University of Medicine and Dentistry, Delegate str., 20/1, Moscow, 127473, Russia
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