Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2025-04-10 DOI:10.1002/aisy.202570018

Aidi Lin, Yuanyuan Peng, Tian Lin, Jun Dai, Jizhu Li, Tingkun Shi, Xixuan Ke, Xulong Liao, Danqi Fang, Man Chen, Huiyu Liang, Shirong Chen, Honghe Xia, Jingtao Wang, Zehua Jiang, Tao Li, Dan Liang, Shanshan Yu, Jing Luo, Ling Gao, Dawei Sun, Yih Chung Tham, Xinjian Chen, Haoyu Chen

{"title":"Assistance of Artificial Intelligence in Diagnosis of Vitreoretinal Lymphoma on Optical Coherence Tomography","authors":"Aidi Lin, Yuanyuan Peng, Tian Lin, Jun Dai, Jizhu Li, Tingkun Shi, Xixuan Ke, Xulong Liao, Danqi Fang, Man Chen, Huiyu Liang, Shirong Chen, Honghe Xia, Jingtao Wang, Zehua Jiang, Tao Li, Dan Liang, Shanshan Yu, Jing Luo, Ling Gao, Dawei Sun, Yih Chung Tham, Xinjian Chen, Haoyu Chen","doi":"10.1002/aisy.202570018","DOIUrl":null,"url":null,"abstract":"<p><b>Automatic Diagnosis of Vitreoretinal Lymphoma</b>\n </p><p>Vitreoretinal lymphoma (VRL) is a rare disease that presents significant challenges for both clinical diagnosis and the training of artificial intelligence (AI) model. Haoyu Chen, Xinjian Chen, and co-workers have developed a pioneering AI model capable of detecting VRL from optical coherence tomography (OCT) images. This model leverages knowledge from common diseases to rare ones, overcoming the obstacle of limited training data. This innovative approach to VRL diagnosis holds the potential to minimize diagnostic delays and expedite the initiation of appropriate treatments. More details can be found in article number 2400500.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 4","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202570018","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202570018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Automatic Diagnosis of Vitreoretinal Lymphoma

Vitreoretinal lymphoma (VRL) is a rare disease that presents significant challenges for both clinical diagnosis and the training of artificial intelligence (AI) model. Haoyu Chen, Xinjian Chen, and co-workers have developed a pioneering AI model capable of detecting VRL from optical coherence tomography (OCT) images. This model leverages knowledge from common diseases to rare ones, overcoming the obstacle of limited training data. This innovative approach to VRL diagnosis holds the potential to minimize diagnostic delays and expedite the initiation of appropriate treatments. More details can be found in article number 2400500.

Abstract Image