{"title":"深度学习人工智能在胶囊内镜检查中的应用","authors":"J. Xia, Jun Pan, T. Xia, Z. Liao","doi":"10.3760/CMA.J.ISSN.1007-5232.2019.12.001","DOIUrl":null,"url":null,"abstract":"胶囊内镜经过长期临床验证，其适应证已基本明确。因胶囊内镜一次检查可产生约6万张图像数据，不仅会占用医生大量阅片时间，而且人工阅片产生疲劳后，会增加漏诊率。因此多项图像处理技术依靠人工智能强大的计算能力，不断在胶囊内镜领域被尝试用于胶囊定位与疾病辅助诊断，以减少阅片时间，提高检查效率。本文简要总结在胶囊内镜领域中基于深度学习人工智能技术的应用与其发展前景。","PeriodicalId":10072,"journal":{"name":"中华消化内镜杂志","volume":"36 1","pages":"877-880"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of deep learning artificial intelligence in capsule endoscopy\",\"authors\":\"J. Xia, Jun Pan, T. Xia, Z. Liao\",\"doi\":\"10.3760/CMA.J.ISSN.1007-5232.2019.12.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"胶囊内镜经过长期临床验证，其适应证已基本明确。因胶囊内镜一次检查可产生约6万张图像数据，不仅会占用医生大量阅片时间，而且人工阅片产生疲劳后，会增加漏诊率。因此多项图像处理技术依靠人工智能强大的计算能力，不断在胶囊内镜领域被尝试用于胶囊定位与疾病辅助诊断，以减少阅片时间，提高检查效率。本文简要总结在胶囊内镜领域中基于深度学习人工智能技术的应用与其发展前景。\",\"PeriodicalId\":10072,\"journal\":{\"name\":\"中华消化内镜杂志\",\"volume\":\"36 1\",\"pages\":\"877-880\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中华消化内镜杂志\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3760/CMA.J.ISSN.1007-5232.2019.12.001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中华消化内镜杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3760/CMA.J.ISSN.1007-5232.2019.12.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
After long-term clinical verification, the indications of capsule endoscopy have been basically clear. Due to the fact that a single examination of capsule endoscopy can generate approximately 60000 image data, it not only takes up a large amount of doctor reading time, but also increases the missed diagnosis rate after manual reading fatigue. Therefore, multiple image processing technologies rely on the powerful computing power of artificial intelligence and are constantly being attempted in the field of capsule endoscopy for capsule localization and disease assisted diagnosis, in order to reduce film reading time and improve examination efficiency. This article briefly summarizes the application and development prospects of deep learning artificial intelligence technology in the field of capsule endoscopy.
期刊介绍：
Chinese Journal of Digestive Endoscopy is a high-level medical academic journal specializing in digestive endoscopy, which was renamed Chinese Journal of Digestive Endoscopy in August 1996 from Endoscopy.
Chinese Journal of Digestive Endoscopy mainly reports the leading scientific research results of esophagoscopy, gastroscopy, duodenoscopy, choledochoscopy, laparoscopy, colorectoscopy, small enteroscopy, sigmoidoscopy, etc. and the progress of their equipments and technologies at home and abroad, as well as the clinical diagnosis and treatment experience.
The main columns are: treatises, abstracts of treatises, clinical reports, technical exchanges, special case reports and endoscopic complications.
The target readers are digestive system diseases and digestive endoscopy workers who are engaged in medical treatment, teaching and scientific research.
Chinese Journal of Digestive Endoscopy has been indexed by ISTIC, PKU, CSAD, WPRIM.
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