This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

IntroductionThe use of optical coherence tomography (OCT) images is increasing in the medical treatment of age-related macular degeneration (AMD), and thus, the amount of data requiring analysis is increasing. Advances in machine-learning techniques may facilitate processing of large amounts of medical image data. Among deep-learning methods, convolution neural networks (CNNs) show superior image recognition ability. This study aimed to build deep-learning models that could distinguish AMD from healthy OCT scans and to distinguish AMD with and without exudative changes without using a segmentation algorithm.MethodsThis was a cross-sectional observational clinical study. A total of 1621 spectral domain (SD)-OCT images of patients with AMD and a healthy control group were studied. The first CNN model was trained and validated using 1382 AMD images and 239 normal images. The second transfer-learning model was trained and validated with 721 AMD images with exudative changes and 661 AMD images without any exudate. The attention area of the CNN was described as a heat map by class activation mapping (CAM). In the second model, which classified images into AMD with or without exudative changes, we compared the learning stabilization of models using or not using transfer learning.ResultsUsing the first CNN model, we could classify AMD and normal OCT images with 100% sensitivity, 91.8% specificity, and 99.0% accuracy. In the second, transfer-learning model, we could classify AMD as having or not having exudative changes, with 98.4% sensitivity, 88.3% specificity, and 93.9% accuracy. CAM successfully described the heat-map area on the OCT images. Including the transfer-learning model in the second model resulted in faster stabilization than when the transfer-learning model was not included.ConclusionTwo computational deep-learning models were developed and evaluated here; both models showed good performance. Automation of the interpretation process by using deep-learning models can save time and improve efficiency.Trial RegistrationNo15073.

Background: Transcranial direct current stimulation (tDCS) is a non-invasive brain modulation technique that has been proved to exert beneficial effects in the acute phase of stroke. To explore the underlying mechanism, we investigated the neuroprotective effects of cathodal tDCS on brain injury caused by middle cerebral artery occlusion (MCAO). Results: We established the MCAO model and sham MCAO model with an epicranial electrode implanted adult male Sprague-Dawley rats, and then they were randomly divided into four groups (MCAO + tDCS, MCAO + sham tDCS (Sham), Control + tDCS and Control + Sham group). In this study, the severity degree of neurological deficit, the morphology of brain damage, the apoptosis, the level of neuron-specific enolase and inflammatory factors, the activation of glial cells was detected. The results showed that cathodal tDCS significantly improved the level of neurological deficit and the brain morphology, reduced the brain damage area and apoptotic index, and increased the number of Nissl body in MCAO rats, compared with MCAO + Sham group. Meanwhile, the high level of NSE, inflammatory factors, Caspase 3 and Bax/Bcl2 ratio in MCAO rats was reduced by cathodal tDCS. Additionally, cathodal tDCS inhibited the activation of astrocyte and microglia induced by MCAO. No difference was found in two Control groups. Conclusion: Our results suggested that cathodal tDCS could accelerate the recovery of neurologic deficit and brain damage caused by MCAO. The inhibition of neuroinflammation and apoptosis resulted from cathodal tDCS may be involved in the neuroprotective process.