Ocular Fundus Diseases Group of Chinese Ophthalmological Society; Expert Group for Artificial Intelligence Research, Development, and Application
{"title":"标准化设计与应用指南:基于眼底彩色摄影的黄斑病变征象初级人工智能筛查系统","authors":"Ocular Fundus Diseases Group of Chinese Ophthalmological Society; Expert Group for Artificial Intelligence Research, Development, and Application","doi":"10.1016/j.imed.2023.05.001","DOIUrl":null,"url":null,"abstract":"<div><p>With the popularity and development of artificial intelligence (AI), disease screening systems based on AI algorithms are gradually emerging in the medical field. Such systems can be used for primary screening of diseases to relieve the pressure on primary health care. In recent years, AI algorithms have demonstrated good performance in the analysis and identification of lesion signs in the macular region of fundus color photography, and a screening system for fundus lesion signs applicable to primary screening is bound to emerge in the future. Therefore, to standardize the design and clinical application of macular region lesion sign screening systems based on AI algorithms, the Ocular Fundus Diseases Group of Chinese Ophthalmological Society, in collaboration with relevant experts, developed this guideline after investigating issues, discussing production evidence, and holding guideline workshops. It aimed to establish uniform standards for the definition of the macular region and lesion signs, AI adoption scenarios, algorithm model construction, dataset establishment and labeling, architecture and function design, and image data acquisition for the screening system to guide the implementation of the screening work.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"3 3","pages":"Pages 213-227"},"PeriodicalIF":4.4000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The standardized design and application guidelines: A primary-oriented artificial intelligence screening system of the lesion sign in the macular region based on fundus color photography\",\"authors\":\"Ocular Fundus Diseases Group of Chinese Ophthalmological Society; Expert Group for Artificial Intelligence Research, Development, and Application\",\"doi\":\"10.1016/j.imed.2023.05.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the popularity and development of artificial intelligence (AI), disease screening systems based on AI algorithms are gradually emerging in the medical field. Such systems can be used for primary screening of diseases to relieve the pressure on primary health care. In recent years, AI algorithms have demonstrated good performance in the analysis and identification of lesion signs in the macular region of fundus color photography, and a screening system for fundus lesion signs applicable to primary screening is bound to emerge in the future. Therefore, to standardize the design and clinical application of macular region lesion sign screening systems based on AI algorithms, the Ocular Fundus Diseases Group of Chinese Ophthalmological Society, in collaboration with relevant experts, developed this guideline after investigating issues, discussing production evidence, and holding guideline workshops. It aimed to establish uniform standards for the definition of the macular region and lesion signs, AI adoption scenarios, algorithm model construction, dataset establishment and labeling, architecture and function design, and image data acquisition for the screening system to guide the implementation of the screening work.</p></div>\",\"PeriodicalId\":73400,\"journal\":{\"name\":\"Intelligent medicine\",\"volume\":\"3 3\",\"pages\":\"Pages 213-227\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intelligent medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2667102623000372\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent medicine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667102623000372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
The standardized design and application guidelines: A primary-oriented artificial intelligence screening system of the lesion sign in the macular region based on fundus color photography
With the popularity and development of artificial intelligence (AI), disease screening systems based on AI algorithms are gradually emerging in the medical field. Such systems can be used for primary screening of diseases to relieve the pressure on primary health care. In recent years, AI algorithms have demonstrated good performance in the analysis and identification of lesion signs in the macular region of fundus color photography, and a screening system for fundus lesion signs applicable to primary screening is bound to emerge in the future. Therefore, to standardize the design and clinical application of macular region lesion sign screening systems based on AI algorithms, the Ocular Fundus Diseases Group of Chinese Ophthalmological Society, in collaboration with relevant experts, developed this guideline after investigating issues, discussing production evidence, and holding guideline workshops. It aimed to establish uniform standards for the definition of the macular region and lesion signs, AI adoption scenarios, algorithm model construction, dataset establishment and labeling, architecture and function design, and image data acquisition for the screening system to guide the implementation of the screening work.