{"title":"人工智能在膀胱癌膀胱镜诊断中的应用","authors":"A. Ikeda","doi":"10.2530/jslsm.jslsm-42_0026","DOIUrl":null,"url":null,"abstract":"In the treatment of bladder cancer, oversight of tumor lesions during cystoscopy is a critical problem, leading to a high rate of postoperative intravesical recurrence. One of the reasons for this is the difference in tumor detection abilities among doctors based on their experience and skill. Therefore, we have sought to improve the accuracy of diagnosis and treatment of bladder cancer by developing a cystoscopy support system. The system uses artificial intelligence that recognizes tumor sites and identifies them in images effectively as expert urologists.","PeriodicalId":19350,"journal":{"name":"Nippon Laser Igakkaishi","volume":"107 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cystoscopic Diagnosis of Bladder Cancer Using Artificial Intelligence\",\"authors\":\"A. Ikeda\",\"doi\":\"10.2530/jslsm.jslsm-42_0026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the treatment of bladder cancer, oversight of tumor lesions during cystoscopy is a critical problem, leading to a high rate of postoperative intravesical recurrence. One of the reasons for this is the difference in tumor detection abilities among doctors based on their experience and skill. Therefore, we have sought to improve the accuracy of diagnosis and treatment of bladder cancer by developing a cystoscopy support system. The system uses artificial intelligence that recognizes tumor sites and identifies them in images effectively as expert urologists.\",\"PeriodicalId\":19350,\"journal\":{\"name\":\"Nippon Laser Igakkaishi\",\"volume\":\"107 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nippon Laser Igakkaishi\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2530/jslsm.jslsm-42_0026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nippon Laser Igakkaishi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2530/jslsm.jslsm-42_0026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cystoscopic Diagnosis of Bladder Cancer Using Artificial Intelligence
In the treatment of bladder cancer, oversight of tumor lesions during cystoscopy is a critical problem, leading to a high rate of postoperative intravesical recurrence. One of the reasons for this is the difference in tumor detection abilities among doctors based on their experience and skill. Therefore, we have sought to improve the accuracy of diagnosis and treatment of bladder cancer by developing a cystoscopy support system. The system uses artificial intelligence that recognizes tumor sites and identifies them in images effectively as expert urologists.
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