Detection of diabetic macular oedema patterns with fine-grained image categorisation on optical coherence tomography.

Journal: BMJ open ophthalmology
PMID:

Abstract

PURPOSE: To develop an artificial intelligence (AI) system for detecting pathological patterns of diabetic macular oedema (DME) with fine-grained image categorisation using optical coherence tomography (OCT) images.

Authors

  • Xin Ye
    Department of Stomatology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Wangli Qiu
    Department of Ophthalmology, Shaoxing People's Hospital, Shaoxing, Zhejiang, China.
  • Linzhen Tu
    Communication University of Zhejiang, Hangzhou, Zhejiang, China.
  • Yingjiao Shen
    Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
  • Qian Chen
    Department of Pain Medicine Guizhou Provincial Orthopedics Hospital Guiyang Guizhou China.
  • Xiangrui Lin
    Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
  • Yaqi Wang
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Wenbin Xie
    State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China.
  • Li-Jun Shen
    Eye Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China. slj20101119@163.com.