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Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study by Yong Han; Weiming Li; Mengmeng Liu; Zhiyuan Wu; Feng Zhang; Xiangtong Liu; Lixin Tao; Xia Li; Xiuhua Guo is a Health Professions article available to read on EtoBox.
What is Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study about?
## Background The supervised deep learning approach provides state-of-the-art performance in a variety of fundus image classification tasks, but it is not applicable for screening tasks with numerous or unknown disease types. The unsupervised anomaly detection (AD) approach, which needs only normal samples to develop a model, may be a workable and cost-saving method of screening for ocular diseases. ## Objective This study aimed to develop and evaluate an AD model for detecting ocular diseases on the basis of color fundus images. ## Methods A generative adversarial network–based AD method for detecting possible ocular diseases was developed and evaluated using 90,499 retinal fundus images derived from 4 large-scale real-world data sets. Four other independent external test sets were used for external testing and further analysis of the model’s performance in detecting 6 common ocular diseases (diabetic retinopathy [DR], glaucoma, cataract, age-related macular degeneration, hypertensive retinopathy [HR], and myopia), DR of different severity levels, and 36 categories of abnormal fundus images. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, a
Who reads Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study?
It is typically read by researchers, students, and practitioners in Health Professions.
- Author
- Yong Han; Weiming Li; Mengmeng Liu; Zhiyuan Wu; Feng Zhang; Xiangtong Liu; Lixin Tao; Xia Li; Xiuhua Guo
- Publisher
- JMIR Publications Inc.
- Published
- 2021
- Language
- EN
- Field
- Health Professions (Health Sciences)