Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy

Nagasawa, Toshihiko; Tabuchi, Hitoshi; Masumoto, Hiroki; Enno, Hiroki; Niki, Masanori; Ohara, Zaigen; Yoshizumi, Yuki; Ohsugi, Hideharu; Mitamura, Yoshinori

doi:10.1007/s10792-019-01074-z

Cited by 63 publications

(56 citation statements)

References 31 publications

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“…At present, UWF imaging techniques have had a wide range of applications in the diagnosis of DR. Studies have found that early damage of DR can be observed in the peripheral region of retina, such as the appearance of haemorrhage, IRMA and so on 79 . Optos ultrawidefield (Optos plc) allows the photography to capture a 200‐degree field of view, which accounts for about 80% of the retina.…”

Section: Performance Of Dl‐based Dr Algorithms By Other Screening Devicesmentioning

confidence: 99%

“…The automated algorithm achieved a sensitivity of 90.3%, a specificity of 53.6% and the AUC of 0.851 for detecting referral‐warranted retinopathy. In 2019, Nagasawa et al 79 trained a CNN model to detect treatment‐naïve PDR in UWF photography. The constructed model demonstrated a high sensitivity of 94.7% and a high specificity of 97.2%, with an AUC of 0.969.…”

Section: Performance Of Dl‐based Dr Algorithms By Other Screening Devicesmentioning

confidence: 99%

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Progress of artificial intelligence in diabetic retinopathy screening

Wang

Yang

et al. 2020

Diabetes Metabolism Res

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Diabetic retinopathy (DR) is one of the leading causes of blindness worldwide, and the limited availability of qualified ophthalmologists restricts its early diagnosis. For the past few years, artificial intelligence technology has developed rapidly and has been applied in DR screening. The upcoming technology provides support on DR screening and improves the identification of DR lesions with a high sensitivity and specificity. This review aims to summarize the progress on automatic detection and classification models for the diagnosis of DR.

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Section: Performance Of Dl‐based Dr Algorithms By Other Screening Devicesmentioning

confidence: 99%

Section: Performance Of Dl‐based Dr Algorithms By Other Screening Devicesmentioning

confidence: 99%

Progress of artificial intelligence in diabetic retinopathy screening

Wang

Yang

et al. 2020

Diabetes Metabolism Res

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“…Retinal vein occlusion, high myopia, glaucoma, and diabetic retinopathy (DR) are common ophthalmological diseases that can usually be diagnosed through fundus images. [1][2][3] Retinal vein occlusion is the most common fundus vascular disease in the elderly and treatment timely may avoid blindness. There are many groups of myopia in China.…”

Section: Introductionmentioning

confidence: 99%

Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images

Zheng

Jiang

et al. 2021

Trans. Vis. Sci. Tech.

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Purpose The discrepancy of the number between ophthalmologists and patients in China is large. Retinal vein occlusion (RVO), high myopia, glaucoma, and diabetic retinopathy (DR) are common fundus diseases. Therefore, in this study, a five-category intelligent auxiliary diagnosis model for common fundus diseases is proposed; the model's area of focus is marked. Methods A total of 2000 fundus images were collected; 3 different 5-category intelligent auxiliary diagnosis models for common fundus diseases were trained via different transfer learning and image preprocessing techniques. A total of 1134 fundus images were used for testing. The clinical diagnostic results were compared with the diagnostic results. The main evaluation indicators included sensitivity, specificity, F1-score, area under the concentration-time curve (AUC), 95% confidence interval (CI), kappa, and accuracy. The interpretation methods were used to obtain the model's area of focus in the fundus image. Results The accuracy rates of the 3 intelligent auxiliary diagnosis models on the 1134 fundus images were all above 90%, the kappa values were all above 88%, the diagnosis consistency was good, and the AUC approached 0.90. For the 4 common fundus diseases, the best results of sensitivity, specificity, and F1-scores of the 3 models were 88.27%, 97.12%, and 84.02%; 89.94%, 99.52%, and 93.90%; 95.24%, 96.43%, and 85.11%; and 88.24%, 98.21%, and 89.55%, respectively. Conclusions This study designed a five-category intelligent auxiliary diagnosis model for common fundus diseases. It can be used to obtain the diagnostic category of fundus images and the model's area of focus. Translational Relevance This study will help the primary doctors to provide effective services to all ophthalmologic patients.

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“…UWF, otherwise known as Optos (Optos 200Tx; Optos Plc, Dunfermline, United Kingdom), is a non-contact, noninvasive imaging modality that can capture up to 200°of visible fundus and has become essential for understanding and managing the peripheral retinal pathologies of adult diseases such as diabetes and retinal vein occlusions [16,17]. Indeed, one report showed the accuracy of UWF-based AI in the detection of DR [18].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography

Nagasawa¹,

Tabuchi

Masumoto³

et al. 2021

Journal of Ophthalmology

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Purpose. The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images. Method. The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography (OCTA) images that passed an image-quality review and were graded as no apparent DR (NDR; 169 images), mild nonproliferative DR (NPDR; 76 images), moderate NPDR (54 images), severe NPDR (90 images), and proliferative DR (PDR; 102 images) by three retinal experts by the International Clinical Diabetic Retinopathy Severity Scale. The findings of tests 1 and 2 to identify no apparent diabetic retinopathy (NDR) and PDR, respectively, were then assessed. For each verification, Optos, OCTA, and Optos OCTA imaging scans with DCNN were performed. Result. The Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and DR showed mean areas under the curve (AUC) of 0.79, 0.883, and 0.847; sensitivity rates of 80.9%, 83.9%, and 78.6%; and specificity rates of 55%, 71.6%, and 69.8%, respectively. Meanwhile, the Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and PDR showed mean AUC of 0.981, 0.928, and 0.964; sensitivity rates of 90.2%, 74.5%, and 80.4%; and specificity rates of 97%, 97%, and 96.4%, respectively. Conclusion. The combination of Optos and OCTA imaging with DCNN could detect DR at desirable levels of accuracy and may be useful in clinical practice and retinal screening. Although the combination of multiple imaging techniques might overcome their individual weaknesses and provide comprehensive imaging, artificial intelligence in classifying multimodal images has not always produced accurate results.

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Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy

Cited by 63 publications

References 31 publications

Progress of artificial intelligence in diabetic retinopathy screening

Progress of artificial intelligence in diabetic retinopathy screening

Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images

Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography

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