Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.

Journal: Communications biology
Published Date: Oct 26, 2021

Abstract

Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale.

Authors

  • Li Lu
    Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Enliang Zhou
    Department of Ophthalmology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China.
  • Wangshu Yu
    Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Bin Chen
    Department of Otorhinolaryngology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai 200233, China.
  • Peifang Ren
    Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Qianyi Lu
    Department of Ophthalmology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Dian Qin
    College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China.
  • Lixian Lu
    College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China.
  • Qin He
    Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, Med-X Center for Materials, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China. Electronic address: qinhe@scu.edu.cn.
  • Xuyuan Tang
    Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Miaomiao Zhu
    Department of Ophthalmology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Wei Han
    Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, The Key Laboratory of New Drug Pharmacology and Toxicology, Ministry of Education, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang, Hebei, China.

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