Artificial intelligence assisted pterygium diagnosis:current status and perspectives  

作　　者：Bang Chen Xin-Wen Fang Mao-Nian Wu Shao-Jun Zhu Bo Zheng Bang-Quan Liu Tao Wu Xiang-Qian Hong Jian-Tao Wang Wei-Hua Yang 

机构地区：[1]School of Information Engineering,Huzhou University,Huzhou 313000,Zhejiang Province,China [2]Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources,Huzhou 313000,Zhejiang Province,China [3]College of Digital Technology and Engineering,Ningbo University of Finance&Economics,Ningbo 315000,Zhejiang Province,China [4]Huzhou Institute,Zhejiang University of Technology,Huzhou 313000,Zhejiang Province,China [5]Shenzhen Eye Institute,Shenzhen Eye Hospital,Jinan University,Shenzhen 518040,Guangdong Province,China

出　　处：《International Journal of Ophthalmology(English edition)》2023年第9期1386-1394,共9页国际眼科杂志（英文版）

基　　金：Supported by National Natural Science Foundation of China(No.61906066);Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202250196);Zhejiang Provincial Philosophy and Social Science Planning Project(No.21NDJC021Z);Natural Science Foundation of Ningbo City(No.202003N4072);Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014);Sanming Project of Medicine in Shenzhen(No.SZSM202011015);Shenzhen Fundamental Research Program(No.JCYJ20220818103207015).

摘　　要：Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

关 键 词：PTERYGIUM intelligent diagnosis artificial intelligence deep learning machine learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R774.1[自动化与计算机技术—控制科学与工程]