Abstract
Purpose—This study was conducted to develop an automated detection algorithm for screening fundus abnormalities, including age-related macular degeneration (AMD), diabetic retinopathy (DR), epiretinal membrane (ERM), retinal vascular occlusion (RVO), and suspected glaucoma among health screening program participants. Methods—The development dataset consisted of 43,221 retinal fundus photographs (from 25,564 participants, mean age 53.38 ± 10.97 years, female 39.0%) from a health screening program and patients of the Kangbuk Samsung Hospital Ophthalmology Department from 2006 to 2017. We evaluated our screening algorithm on independent validation datasets. Five separate one-versus-rest (OVR) classification algorithms based on deep convolutional neural networks (CNNs) were trained to detect AMD, ERM, DR, RVO, and suspected glaucoma. The ground truth for both development and validation datasets was graded at least two times by three ophthalmologists. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated for each disease, as well as their macro-averages. Results—For the internal validation dataset, the average sensitivity was 0.9098 (95% confidence interval (CI), 0.8660–0.9536), the average specificity was 0.9079 (95% CI, 0.8576–0.9582), and the overall accuracy was 0.9092 (95% CI, 0.8769–0.9415). For the external validation dataset consisting of 1698 images, the average of the AUCs was 0.9025 (95% CI, 0.8671–0.9379). Conclusions—Our algorithm had high sensitivity and specificity for detecting major fundus abnormalities. Our study will facilitate expansion of the applications of deep learning-based computer-aided diagnostic decision support tools in actual clinical settings. Further research is needed to improved generalization for this algorithm.
Highlights
Deep learning has yielded substantial results in other areas of research, the development of a deep learning-based diabetic retinopathy detection algorithm by Google may have been the first time that most ophthalmologists acknowledged the potential role of deep learning-based applications in the clinical setting [1,2,3]
We do not need a screening tool to determine only whether a patient has diabetic retinopathy; instead, we need a diagnostic tool that can reliably determine whether patients have any abnormal ocular findings and provide specific diagnoses, including multiple diagnoses for a single patient
Grading and Annotation Process To enhance the performances of detailed diagnosis, we developed an annotation tool which resembled an Early Treatment Diabetic Retinopathy Study grid divided to 20 sectors centering out from fovea to periphery with a maximum diameter of 8000 μm (Figure 2.)
Summary
Deep learning has yielded substantial results in other areas of research, the development of a deep learning-based diabetic retinopathy detection algorithm by Google may have been the first time that most ophthalmologists acknowledged the potential role of deep learning-based applications in the clinical setting [1,2,3]. We do not need a screening tool to determine only whether a patient has diabetic retinopathy; instead, we need a diagnostic tool that can reliably determine whether patients have any abnormal ocular findings and provide specific diagnoses, including multiple diagnoses for a single patient. This function is most important in situations where fundus screening tools are used for large patient populations. There is a growing need to develop a grading tool to assist in the screening of fundus photographs that can yield reliable results, thereby minimizing ophthalmologists’ work burden
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