Artificial intelligence assisted pterygium diagnosis: current status and perspectives
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Wei-Hua Yang. Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China. benben0606@139.com

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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).

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    Abstract:

    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.

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Bang Chen, Xin-Wen Fang, Mao-Nian Wu, et al. Artificial intelligence assisted pterygium diagnosis: current status and perspectives. Int J Ophthalmol, 2023,16(9):1386-1394

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Publication History
  • Received:April 17,2023
  • Revised:May 24,2023
  • Adopted:
  • Online: August 22,2023
  • Published: