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.

Purpose 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). Methods 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. Result 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. Conclusion Our findings suggested that PDR could be diagnosed using wide-angle camera images and deep learning.

We investigated the rates of the use of steroids in Japanese central serous chorioretinopathy (CSC) cases and differences in the characteristics of CSC with and without steroids. A total of 538 eyes of 477 patients diagnosed with CSC, with 3 months or more of follow-up between April 2013 and June 2017 at 8 institutions. Patients with CSC with more than 3 months of follow-up were identified by OCT and fluorescein angiography at 8 institutions. Data collected included patient demographics, history of corticosteroid medication and smoking, spherical errors, findings of angiography, subfoveal choroidal thickness, and changes through the follow-up period. Differences in these findings were analyzed in cases with and without corticosteroid treatment. Among the 477 patients (344 men,133 women), 74 (15.5%) (39 men, 35 women) underwent current or prior steroid treatment. Cases with steroids were higher age (p = 0.0403) and showed no male prevalence, more bilateral involvement (p < 0.0001), and the affected eyes had multiple pigment epithelial detachment (p <0.0001), more fluorescein leakage sites (p < 0.0001), greater choroidal thickness (p = 0.0287) and a higher recurrence rate (p = 0.0412). Steroids can cause severer CSC through an effect on choroidal vessels and an impairment of retinal pigment epithelium.

Our results present the current clinical preferences of anti-VEGF treatment for DME in Japan. The best-corrected visual acuity and the retinal thickness are important indicators to institute this therapy. The majority of the ophthalmologists use anti-VEGF treatment as first-line therapy and prefer the 1 + pro re nata regimen.

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.

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning.

The binarisation of the EDI-OCT images can be used to quantify the activity of PCV and to predict the prognosis after IVA.

Purpose: To report the clinical findings of 2 patients with focal choroidal excavation in the macula detected by spectral-domain optical coherence tomography (SD-OCT). Methods: Three eyes of 2 patients with a focal macular choroidal excavation detected by SD-OCT were studied. The eyes were examined by fundus autofluorescence (FAF), fluorescein angiography, fundus-related microperimetry, and multifocal electroretinography (mfERG). Results: In spite of a complaint of metamorphopsia, the visual acuity was normal in 2 eyes. SD-OCT demonstrated a choroidal excavation in the macula but the foveal contour was normal in 3 eyes. The excavation involved the outer retinal layers up to the external limiting membrane in all eyes, and a type 2 secondary choroidal neovascularization (CNV) developed in 1 of the 3 eyes. There were areas of hypoautofluorescence in the FAF images, and areas of decreased retinal sensitivity determined by microperimetry. These areas corresponded to the choroidal excavation in all eyes. The P1 amplitudes of the mfERGs were decreased in the fovea of 1 eye without a CNV. Conclusions: The choroidal excavation remained stable for 3 years in 2 eyes, a secondary CNV developed in 1 eye during the course of the disease. More cases and longer follow-up periods will be necessary to determine the etiology, clinical course, and visual prognosis of eyes with a choroidal excavation.