Development of a deep learning-based image eligibility verification system for detecting and filtering out ineligible fundus images: A multicentre study.

Journal: International journal of medical informatics
Published Date: Dec 13, 2020

Abstract

BACKGROUND: Recent advances in artificial intelligence (AI) have shown great promise in detecting some diseases based on medical images. Most studies developed AI diagnostic systems only using eligible images. However, in real-world settings, ineligible images (including poor-quality and poor-location images) that can compromise downstream analysis are inevitable, leading to uncertainty about the performance of these AI systems. This study aims to develop a deep learning-based image eligibility verification system (DLIEVS) for detecting and filtering out ineligible fundus images.

Authors

  • Zhongwen Li
    Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China.
  • Jiewei Jiang
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Rd, Xi'an, 710071, China.
  • Heding Zhou
    Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China.
  • Qinxiang Zheng
    Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
  • Xiaotian Liu
    School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, P.R.China.
  • Kuan Chen
    Infervision, Beijing, China.
  • Hongfei Weng
    Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.

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