Automated detection of retinal nonperfusion area caused by retinal vein occlusion

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

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

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

Studies of Near Infrared Spectroscopy for P!'edicting Forage Quality of Tropical Grasses

Introduction Near Infrared Reflectance Spectroscopy (NIRS) for forage has been widely and successfully used throughout the world, but the study for tropical grasses is still limited, especially in Asia including Japan and Indonesia. Tropical grasses show some differences from tempera te grasses in lea r ana tomy, chemical composition, cell wall concentrations (AKIN and BURDICK, 1973), and physical property (AKIN et al., 1975). The NIRS analysis is a physical method, non:..destructive measurement relaled to lhe energies absorbed from the incident radiation by molecular groups in the sample (M URRA Y and WILLIAMS, 1990), and might be limited to successful prediction of tropical grasses due to all these differences. This study has been done as prediction data from tropical forages by NIRS analysis in Asia, and for the reasons refered above. The aim of this study is to evaluate NIRS in prediction of nutrient content, such as moisture, protein, crude fiber, crude fat, ash, NFE, ADF, ADL, silica, organic matter, OCW, OCC, Oa, and Ob of tropical grasses compared with the previous study conducted in other forages

Improvement of Automatic BBS Visualization in T2V : Animation considering dialogue structure

T2V(Text-to-Vision) a technology which is capable of automatic animated movie generation to assist individuals who do not have special knowledge about animation production. This paper shows improvement of the function that animates BBS (Bulletin Board System) with this technology. T2V has a package (2ch convertor) that is capable of animating “2channel” (largest BBS in Japan). The present 2ch convertor, however, does not support dialogue situation based on quotation marks. Therefore, it causes a problem that it cannot produce animation with dialogue. In this paper we propose a method of animation production regarding the conversation structure in BBS to create more natural expression in the animation

Improvement of Automatic BBS Visualization in T2V : Animation considering dialogue structure

T2V(Text-to-Vision) a technology which is capable of automatic animated movie generation to assist individuals who do not have special knowledge about animation production. This paper shows improvement of the function that animates BBS (Bulletin Board System) with this technology. T2V has a package (2ch convertor) that is capable of animating “2channel” (largest BBS in Japan). The present 2ch convertor, however, does not support dialogue situation based on quotation marks. Therefore, it causes a problem that it cannot produce animation with dialogue. In this paper we propose a method of animation production regarding the conversation structure in BBS to create more natural expression in the animation

Improvement of Automatic BBS Visualization in T2V : Animation considering dialogue structure

T2V(Text-to-Vision) a technology which is capable of automatic animated movie generation to assist individuals who do not have special knowledge about animation production. This paper shows improvement of the function that animates BBS (Bulletin Board System) with this technology. T2V has a package (2ch convertor) that is capable of animating “2channel” (largest BBS in Japan). The present 2ch convertor, however, does not support dialogue situation based on quotation marks. Therefore, it causes a problem that it cannot produce animation with dialogue. In this paper we propose a method of animation production regarding the conversation structure in BBS to create more natural expression in the animation

Production of virus-specific antiserum corresponding to sequences in the lactate dehydrogenase-elevating virus (LDV) ORF6 protein.

The elucidation of the antigenic structure of the envelope proteins of Arteriviridae which includes lactate dehydrogenase-elevating virus (LDV) will provide further understanding of a mechanism of strict host cell specificity. To analyze the linkage between LDV envelope proteins, M/VP-2 and VP-3, which may play an important role in viral infectivity, we generated specific antibody against M/VP-2 that has not been reported in previous studies. A synthetic polypeptide corresponding to the C-terminal region of LDV strain C (LDV-C) ORF6, which encodes M/VP-2, was chemically synthesized and coupled to keyhole limpet hemocyanin (KLH). The peptide was immunogenic in rabbits and induced antibody specific for viral protein. Western blotting and immunofluorescence analysis of virion M/VP-2 in infected macrophages showed that the antibody was able to react specifically with authentic virion protein. The immunoreactive antibody against LDV M/VP-2 described in this study will be useful for further studies of the specific roles of the envelope proteins in arterivirus assembly and infectivity