Artificial intelligence assisted pterygium diagnosis: current status and perspectives
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 o...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Press of International Journal of Ophthalmology (IJO PRESS)
2023-09-01
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| Series: | International Journal of Ophthalmology |
| Subjects: | |
| Online Access: | http://ies.ijo.cn/en_publish/2023/9/20230904.pdf |
| _version_ | 1797739535199633408 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Bang Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T13:59:32Z |
| format | Article |
| id | doaj.art-3053296b6f27446d852819bf4eb52b64 |
| institution | Directory Open Access Journal |
| issn | 2222-3959 2227-4898 |
| language | English |
| last_indexed | 2024-03-12T13:59:32Z |
| publishDate | 2023-09-01 |
| publisher | Press of International Journal of Ophthalmology (IJO PRESS) |
| record_format | Article |
| series | International Journal of Ophthalmology |
| spelling | doaj.art-3053296b6f27446d852819bf4eb52b642023-08-22T08:47:16ZengPress of International Journal of Ophthalmology (IJO PRESS)International Journal of Ophthalmology2222-39592227-48982023-09-011691386139410.18240/ijo.2023.09.0420230904Artificial intelligence assisted pterygium diagnosis: current status and perspectivesBang Chen0Xin-Wen Fang1Mao-Nian Wu2Shao-Jun Zhu3Bo Zheng4Bang-Quan Liu5Tao Wu6Xiang-Qian Hong7Jian-Tao Wang8Wei-Hua Yang9Wei-Hua Yang. Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China. benben0606@139.comSchool of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, ChinaCollege of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo 315000, Zhejiang Province, ChinaHuzhou Institute, Zhejiang University of Technology, Huzhou 313000, Zhejiang Province, ChinaShenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, ChinaShenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, ChinaShenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, ChinaPterygium 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.http://ies.ijo.cn/en_publish/2023/9/20230904.pdfpterygiumintelligent diagnosisartificial intelligencedeep learningmachine learning |
| spellingShingle | 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 Artificial intelligence assisted pterygium diagnosis: current status and perspectives International Journal of Ophthalmology pterygium intelligent diagnosis artificial intelligence deep learning machine learning |
| title | Artificial intelligence assisted pterygium diagnosis: current status and perspectives |
| title_full | Artificial intelligence assisted pterygium diagnosis: current status and perspectives |
| title_fullStr | Artificial intelligence assisted pterygium diagnosis: current status and perspectives |
| title_full_unstemmed | Artificial intelligence assisted pterygium diagnosis: current status and perspectives |
| title_short | Artificial intelligence assisted pterygium diagnosis: current status and perspectives |
| title_sort | artificial intelligence assisted pterygium diagnosis current status and perspectives |
| topic | pterygium intelligent diagnosis artificial intelligence deep learning machine learning |
| url | http://ies.ijo.cn/en_publish/2023/9/20230904.pdf |
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