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Artificial intelligence assisted pterygium diagnosis: current status and perspectives.
Chen, Bang; Fang, Xin-Wen; Wu, Mao-Nian; Zhu, Shao-Jun; Zheng, Bo; Liu, Bang-Quan; Wu, Tao; Hong, Xiang-Qian; Wang, Jian-Tao; Yang, Wei-Hua.
Afiliación
  • Chen B; School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China.
  • Fang XW; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China.
  • Wu MN; School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China.
  • Zhu SJ; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China.
  • Zheng B; School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China.
  • Liu BQ; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China.
  • Wu T; School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China.
  • Hong XQ; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China.
  • Wang JT; School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China.
  • Yang WH; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China.
Int J Ophthalmol ; 16(9): 1386-1394, 2023.
Article en En | MEDLINE | ID: mdl-37724272
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Int J Ophthalmol Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: China
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Int J Ophthalmol Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: China