{"title":"在医疗设备上集成深度学习模型的青光眼早期检测。","authors":"DilipKumar Jang Bahadur Saini , Siddhartha Choubey , Abha Choubey , Mariyam Kidwai , Monica Mehrotra , Sagar Kolekar , Yudhishthir Raut","doi":"10.1016/j.biosystems.2024.105156","DOIUrl":null,"url":null,"abstract":"<div><p>The early detection of some diseases can be a decisive factor in postponing or stabilizing their most adverse effects on the people who suffer from them. In the case of glaucoma, which is an ocular pathology that is the second leading cause of blindness in the world, early detection can make the difference between a patient’s complete losses of vision, or preserve their sight, as well as improve their subsequent treatment. It is for this reason that there are currently medical campaigns for the early detection of pathologies with these characteristics in a certain study population, called screening, which have shown very good results. In addition, the application of telemedicine to these processes has allowed remote evaluation of cases by clinical experts and numerous initiatives have emerged for its use in new screening strategies. On the other hand, biomedical image processing techniques based on deep learning have undergone great development in recent years, and there are several works that have demonstrated their possible application in the automatic detection of glaucoma with fundus images. The article has consisted of the development of a web platform that integrates both scenarios: on the one hand, the remote evaluation of fundus images by medical specialists, and on the other, the application of a tool based on Deep Learning for the automatic detection of glaucoma in the case studies.</p></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Early detection of glaucoma integrated with deep learning models over medical devices\",\"authors\":\"DilipKumar Jang Bahadur Saini , Siddhartha Choubey , Abha Choubey , Mariyam Kidwai , Monica Mehrotra , Sagar Kolekar , Yudhishthir Raut\",\"doi\":\"10.1016/j.biosystems.2024.105156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The early detection of some diseases can be a decisive factor in postponing or stabilizing their most adverse effects on the people who suffer from them. In the case of glaucoma, which is an ocular pathology that is the second leading cause of blindness in the world, early detection can make the difference between a patient’s complete losses of vision, or preserve their sight, as well as improve their subsequent treatment. It is for this reason that there are currently medical campaigns for the early detection of pathologies with these characteristics in a certain study population, called screening, which have shown very good results. In addition, the application of telemedicine to these processes has allowed remote evaluation of cases by clinical experts and numerous initiatives have emerged for its use in new screening strategies. On the other hand, biomedical image processing techniques based on deep learning have undergone great development in recent years, and there are several works that have demonstrated their possible application in the automatic detection of glaucoma with fundus images. The article has consisted of the development of a web platform that integrates both scenarios: on the one hand, the remote evaluation of fundus images by medical specialists, and on the other, the application of a tool based on Deep Learning for the automatic detection of glaucoma in the case studies.</p></div>\",\"PeriodicalId\":50730,\"journal\":{\"name\":\"Biosystems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0303264724000418\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0303264724000418","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
Early detection of glaucoma integrated with deep learning models over medical devices
The early detection of some diseases can be a decisive factor in postponing or stabilizing their most adverse effects on the people who suffer from them. In the case of glaucoma, which is an ocular pathology that is the second leading cause of blindness in the world, early detection can make the difference between a patient’s complete losses of vision, or preserve their sight, as well as improve their subsequent treatment. It is for this reason that there are currently medical campaigns for the early detection of pathologies with these characteristics in a certain study population, called screening, which have shown very good results. In addition, the application of telemedicine to these processes has allowed remote evaluation of cases by clinical experts and numerous initiatives have emerged for its use in new screening strategies. On the other hand, biomedical image processing techniques based on deep learning have undergone great development in recent years, and there are several works that have demonstrated their possible application in the automatic detection of glaucoma with fundus images. The article has consisted of the development of a web platform that integrates both scenarios: on the one hand, the remote evaluation of fundus images by medical specialists, and on the other, the application of a tool based on Deep Learning for the automatic detection of glaucoma in the case studies.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.