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

查看原文本刊更多论文

人工智能在光学相干断层成像诊断玻璃体视网膜淋巴瘤中的辅助作用

玻璃体视网膜淋巴瘤（VRL）是一种罕见的疾病，对临床诊断和人工智能（AI）模型的训练都提出了重大挑战。陈浩宇、陈新建及其同事开发了一种开创性的人工智能模型，能够从光学相干断层扫描（OCT）图像中检测VRL。该模型利用了从常见病到罕见病的知识，克服了训练数据有限的障碍。这种创新性的VRL诊断方法有可能最大限度地减少诊断延误并加快适当治疗的启动。更多细节见第2400500条。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊