Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning


Autoři: Daisuke Nagasato aff001;  Hitoshi Tabuchi aff001;  Hiroki Masumoto aff001;  Hiroki Enno aff002;  Naofumi Ishitobi aff001;  Masahiro Kameoka aff001;  Masanori Niki aff003;  Yoshinori Mitamura aff003
Působiště autorů: Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan aff001;  Rist Incorporated, Tokyo, Japan aff002;  Department of Ophthalmology, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223965

Souhrn

We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening.

Klíčová slova:

Angiography – Capillaries – Diabetic retinopathy – Eyes – Neural networks – Retinal vessels – Support vector machines – Tomography


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2019 Číslo 11