Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images.

Journal: Ophthalmology. Retina
Published Date:

Abstract

OBJECTIVE: We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images.

Authors

  • Xu Han Shi
    Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Lie Ju
  • Li Dong
    Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China. donglikn199@163.com.
  • Rui Heng Zhang
    Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Lei Shao
    Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China.
  • Yan Ni Yan
    Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
  • Ya Xing Wang
    Beijing Visual Science and Translational Eye Research Institute (BERI), Eye Center of Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
  • Xue Fei Fu
    Beijing Airdoc Technology Co., Ltd., Beijing, China.
  • Yu Zhong Chen
    Beijing Airdoc Technology Co., Ltd., Beijing, China.
  • Zong Yuan Ge
    Beijing Airdoc Technology Co., Ltd., Beijing, China; Augmented Intelligence and Multimodal Analytics (AIM) for Health Lab, Faculty of Information Technology, Monash University, Clayton, Australia; Faculty of Engineering, Monash University, Clayton, Australia.
  • Wen Bin Wei
    Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.