Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy

Abstract

The aim of this study is to assess the performance of two machine-learning technologies, namely, deep learning (DL) and support vector machine (SVM) algorithms, for detecting central retinal vein occlusion (CRVO) in ultrawide-field fundus images. Images from 125 CRVO patients (n=125 images) and 202 non-CRVO normal subjects (n=238 images) were included in this study. Training to construct the DL model using deep convolutional neural network algorithms was provided using ultrawide-field fundus images. The SVM uses scikit-learn library with a radial basis function kernel. The diagnostic abilities of DL and the SVM were compared by assessing their sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve for CRVO. For diagnosing CRVO, the DL model had a sensitivity of 98.4% (95% confidence interval (CI), 94.3–99.8%) and a specificity of 97.9% (95% CI, 94.6–99.1%) with an AUC of 0.989 (95% CI, 0.980–0.999). In contrast, the SVM model had a sensitivity of 84.0% (95% CI, 76.3–89.3%) and a specificity of 87.5% (95% CI, 82.7–91.1%) with an AUC of 0.895 (95% CI, 0.859–0.931). Thus, the DL model outperformed the SVM model in all indices assessed (P < 0.001 for all). Our data suggest that a DL model derived using ultrawide-field fundus images could distinguish between normal and CRVO images with a high level of accuracy and that automatic CRVO detection in ultrawide-field fundus ophthalmoscopy is possible. This proposed DL-based model can also be used in ultrawide-field fundus ophthalmoscopy to accurately diagnose CRVO and improve medical care in remote locations where it is difficult for patients to attend an ophthalmic medical center.

Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning

“…This method is similar to that used in the previous studies 18,21 . www.nature.com/scientificreports/ Brachial-ankle PWV measurement.…”

Promising Artificial Intelligence–Machine Learning–Deep Learning Algorithms in Ophthalmology

“…It is certain that promising computer algorithms will make retinal disorders more objective than before. In addition to DR, 14,21,[23][24][25] AMD, [41][42][43][44][45] and glaucoma 10,[50][51][52][53] ; AI, ML, and DL has also been used to diagnose other retinal diseases, including central retinal vein occlusion (CRVO), 57 rhegmatogenous retinal detachment (RRD), 58 retinopathy of prematurity (ROP), 59 and reticular pseudodrusen. 36 Apart from retina, AI-based systems have been improved in order to better identify or appraise other ophthalmic disorders, including paediatric cataract, 60 keratoconus (KC), 61 corneal ectasia, 62 oculoplastic reconstruction 63 , evaluation of corneal power after myopic corneal refractive surgery, 64 making surgical plans in horizontal strabismus, 65 and determining of pigment epithelium detachment in polypoidal choroidal vasculopathy (PVC).…”

“…It has been indicated that DL model has higher sensitivity, specificity, and AUC values for detecting CRVO in Optos fundus photographs. 57 This technology may have an important potential clinical benefit to reach large areas without retina specialists. 57 Therefore, early diagnosis and intervention of CRVO patients living in areas with inadequate ophthalmic care is very crucial for visual recovery.…”

“…57 This technology may have an important potential clinical benefit to reach large areas without retina specialists. 57 Therefore, early diagnosis and intervention of CRVO patients living in areas with inadequate ophthalmic care is very crucial for visual recovery. As with other ophthalmic diseases, automatic diagnosis in CRVO will also be potentially beneficial for both patients and ophthalmologists.…”

“…As with other ophthalmic diseases, automatic diagnosis in CRVO will also be potentially beneficial for both patients and ophthalmologists. 57 RRD, a severe condition, causes visual loss. Therefore, the early diagnosis and treatment of RRD is crucial.…”