{"title":"人工智能和机器学习是眼部附件肿瘤诊断的新领域:综述。","authors":"Qirat Qurban, Lorraine Cassidy","doi":"10.1177/20503121241274197","DOIUrl":null,"url":null,"abstract":"<p><p>In our article, we explore the transformative potential of Artificial Intelligence and Machine Learning in oculo-oncology, focusing on the diagnosis and management of ocular adnexal tumors. Delving into the intricacies of adnexal conditions such as conjunctival melanoma and squamous conjunctival carcinoma, the study emphasizes recent breakthroughs, such as Artificial Intelligence-driven early detection methods. While acknowledging challenges like the scarcity of specialized datasets and issues in standardizing image capture, the research underscores encouraging patient acceptance, as demonstrated in melanoma diagnosis studies. The abstract calls for overcoming obstacles, conducting clinical trials, establishing global regulatory norms and fostering collaboration between ophthalmologists and Artificial Intelligence experts. Overall, the article envisions Artificial Intelligence's imminent transformative impact on ocular and periocular cancer diagnosis.</p>","PeriodicalId":21398,"journal":{"name":"SAGE Open Medicine","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350536/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and machine learning a new frontier in the diagnosis of ocular adnexal tumors: A review.\",\"authors\":\"Qirat Qurban, Lorraine Cassidy\",\"doi\":\"10.1177/20503121241274197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In our article, we explore the transformative potential of Artificial Intelligence and Machine Learning in oculo-oncology, focusing on the diagnosis and management of ocular adnexal tumors. Delving into the intricacies of adnexal conditions such as conjunctival melanoma and squamous conjunctival carcinoma, the study emphasizes recent breakthroughs, such as Artificial Intelligence-driven early detection methods. While acknowledging challenges like the scarcity of specialized datasets and issues in standardizing image capture, the research underscores encouraging patient acceptance, as demonstrated in melanoma diagnosis studies. The abstract calls for overcoming obstacles, conducting clinical trials, establishing global regulatory norms and fostering collaboration between ophthalmologists and Artificial Intelligence experts. Overall, the article envisions Artificial Intelligence's imminent transformative impact on ocular and periocular cancer diagnosis.</p>\",\"PeriodicalId\":21398,\"journal\":{\"name\":\"SAGE Open Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350536/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAGE Open Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/20503121241274197\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAGE Open Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/20503121241274197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Artificial intelligence and machine learning a new frontier in the diagnosis of ocular adnexal tumors: A review.
In our article, we explore the transformative potential of Artificial Intelligence and Machine Learning in oculo-oncology, focusing on the diagnosis and management of ocular adnexal tumors. Delving into the intricacies of adnexal conditions such as conjunctival melanoma and squamous conjunctival carcinoma, the study emphasizes recent breakthroughs, such as Artificial Intelligence-driven early detection methods. While acknowledging challenges like the scarcity of specialized datasets and issues in standardizing image capture, the research underscores encouraging patient acceptance, as demonstrated in melanoma diagnosis studies. The abstract calls for overcoming obstacles, conducting clinical trials, establishing global regulatory norms and fostering collaboration between ophthalmologists and Artificial Intelligence experts. Overall, the article envisions Artificial Intelligence's imminent transformative impact on ocular and periocular cancer diagnosis.