Development and validation of a deep learning system to classify aetiology and predict anatomical outcomes of macular hole

AIMS: To develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month after vitrectomy and internal limiting membrane peelin...

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Detalles Bibliográficos
Autores principales: Xiao, Yu, Hu, Yijun, Quan, Wuxiu, Yang, Yahan, Lai, Weiyi, Wang, Xun, Zhang, Xiayin, Zhang, Bin, Wu, Yuqing, Wu, Qiaowei, Liu, Baoyi, Zeng, Xiaomin, Lin, Zhanjie, Fang, Ying, Hu, Yu, Feng, Songfu, Yuan, Ling, Cai, Hongmin, Li, Tao, Lin, Haotian, Yu, Honghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Clinical Science
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763201/
https://www.ncbi.nlm.nih.gov/pubmed/34348922
http://dx.doi.org/10.1136/bjophthalmol-2021-318844

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763201/
https://www.ncbi.nlm.nih.gov/pubmed/34348922
http://dx.doi.org/10.1136/bjophthalmol-2021-318844

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