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

Journal: The British journal of ophthalmology
Published Date:

Abstract

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 peeling (VILMP).

Authors

  • Yu Xiao
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Yijun Hu
    Aier Institute of Refractive Surgery, Refractive Surgery Center, Guangzhou Aier Eye Hospital, Guangzhou, China.
  • Wuxiu Quan
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
  • Yahan Yang
    University of Pennsylvania, Philadelphia, PA.
  • Weiyi Lai
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
  • Xun Wang
    College of Computer Science and Technology, China University of Petroleum, Dongying, China.
  • Xiayin Zhang
    State Key Laboratory of Ophthalmology, Clinical Research Center for Ocular Disease, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China.
  • Bin Zhang
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Yuqing Wu
    BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.
  • Qiaowei Wu
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Baoyi Liu
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Xiaomin Zeng
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
  • Zhanjie Lin
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
  • Ying Fang
    College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
  • Yu Hu
    Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Songfu Feng
    Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, China.
  • Ling Yuan
    Department of Obstetrics, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 201204, China.
  • Hongmin Cai
    School of Computer Science& Engineering, South China University of Technology, Guangdong, China. hmcai@scut.edu.cn.
  • Tao Li
    Department of Emergency Medicine, Jining No.1 People's Hospital, Jining, China.
  • Haotian Lin
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou.
  • Honghua Yu
    Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, the Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.