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

International Ophthalmology
Toshihiko NagasawaYoshinori Mitamura

Abstract

We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR). We conducted training with the DCNN using 378 photographic images (132 PDR and 246 non-PDR) and constructed a deep learning model. The area under the curve (AUC), sensitivity, and specificity were examined. The constructed deep learning model demonstrated a high sensitivity of 94.7% and a high specificity of 97.2%, with an AUC of 0.969. Our findings suggested that PDR could be diagnosed using wide-angle camera images and deep learning.

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Citations

Oct 2, 2020·JAMA Ophthalmology·Tae Keun Yoo, Joon Yul Choi
Apr 24, 2020·Eye and Vision·Yan TongYin Shen
Aug 3, 2020·Current Opinion in Ophthalmology·Ashley KrasJohn B Miller
Oct 4, 2020·Diabetes/metabolism Research and Reviews·Yue-Lin WangWei-Hong Yu
Feb 13, 2021·Graefe's Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Für Klinische Und Experimentelle Ophthalmologie·Hitoshi ImamuraSachiko Maruoka
Nov 5, 2020·NPJ Digital Medicine·Zhongwen LiHaotian Lin
Dec 11, 2020·NPJ Digital Medicine·Zhongwen LiHaotian Lin
Jun 17, 2021·Translational Vision Science & Technology·Bo ZhengWei-Hua Yang
Aug 8, 2021·Journal of Clinical Medicine·Mohamed AshrafLloyd Paul Aiello
Aug 31, 2021·Journal of Imaging·Vasudevan LakshminarayananJanarthanam Jothi Balaji
Oct 30, 2020·Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift für Augenheilkunde·Kui-Fang DuWen-Bin Wei

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