TY - JOUR
T1 - Application of an anomaly detection model to screen for ocular diseases using color retinal fundus images
T2 - Design and evaluation study
AU - Han, Yong
AU - Li, Weiming
AU - Liu, Mengmeng
AU - Wu, Zhiyuan
AU - Zhang, Feng
AU - Liu, Xiangtong
AU - Tao, Lixin
AU - Li, Xia
AU - Guo, Xiuhua
N1 - Publisher Copyright:
© Yong Han, Weiming Li, Mengmeng Liu, Zhiyuan Wu, Feng Zhang, Xiangtong Liu, Lixin Tao, Xia Li, Xiuhua Guo. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.07.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
PY - 2021/7
Y1 - 2021/7
N2 - 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, and specificity of the model’s performance were calculated and presented. Results: Our model achieved an AUC of 0.896 with 82.69% sensitivity and 82.63% specificity in detecting abnormal fundus images in the internal test set, and it achieved an AUC of 0.900 with 83.25% sensitivity and 85.19% specificity in 1 external proprietary data set. In the detection of 6 common ocular diseases, the AUCs for DR, glaucoma, cataract, AMD, HR, and myopia were 0.891, 0.916, 0.912, 0.867, 0.895, and 0.961, respectively. Moreover, the AD model had an AUC of 0.868 for detecting any DR, 0.908 for detecting referable DR, and 0.926 for detecting vision-threatening DR. Conclusions: The AD approach achieved high sensitivity and specificity in detecting ocular diseases on the basis of fundus images, which implies that this model might be an efficient and economical tool for optimizing current clinical pathways for ophthalmologists. Future studies are required to evaluate the practical applicability of the AD approach in ocular disease screening.
AB - 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, and specificity of the model’s performance were calculated and presented. Results: Our model achieved an AUC of 0.896 with 82.69% sensitivity and 82.63% specificity in detecting abnormal fundus images in the internal test set, and it achieved an AUC of 0.900 with 83.25% sensitivity and 85.19% specificity in 1 external proprietary data set. In the detection of 6 common ocular diseases, the AUCs for DR, glaucoma, cataract, AMD, HR, and myopia were 0.891, 0.916, 0.912, 0.867, 0.895, and 0.961, respectively. Moreover, the AD model had an AUC of 0.868 for detecting any DR, 0.908 for detecting referable DR, and 0.926 for detecting vision-threatening DR. Conclusions: The AD approach achieved high sensitivity and specificity in detecting ocular diseases on the basis of fundus images, which implies that this model might be an efficient and economical tool for optimizing current clinical pathways for ophthalmologists. Future studies are required to evaluate the practical applicability of the AD approach in ocular disease screening.
KW - Anomaly detection
KW - Artificial intelligence
KW - Cataract
KW - Diabetic retinopathy
KW - Disease screening
KW - Eye
KW - Fundus image
KW - Glaucoma
KW - Macular degeneration
KW - Ocular disease
KW - Ophthalmology
UR - http://www.scopus.com/inward/record.url?scp=85110294254&partnerID=8YFLogxK
U2 - 10.2196/27822
DO - 10.2196/27822
M3 - Article
C2 - 34255681
AN - SCOPUS:85110294254
SN - 1438-8871
VL - 23
SP - e27822
JO - Journal of Medical Internet Research
JF - Journal of Medical Internet Research
IS - 7
M1 - e27822
ER -