Macrohistory of Moyamoya Disease Analyzed Using Artificial Intelligence.

INTRODUCTION: Moyamoya disease is characterized by progressive stenotic changes in the terminal segment of the internal carotid artery and the development of abnormal vascular networks called moyamoya vessels. The objective of this review was to provide a holistic view of the epidemiology, etiology, clinical findings, treatment, and pathogenesis of moyamoya disease. A literature search was performed in PubMed using the term "moyamoya disease," for articles published until 2021. RESULTS: Artificial intelligence (AI) clustering was used to classify the articles into 5 clusters: (1) pathophysiology (23.5%); (2) clinical background (37.3%); (3) imaging (13.2%); (4) treatment (17.3%); and (5) genetics (8.7%). Many articles in the "clinical background" cluster were published from the 1970s. However, in the "treatment" and "genetics" clusters, the articles were published from the 2010s through 2021. In 2011, it was confirmed that a gene called Ringin protein 213 (RNF213) is a susceptibility gene for moyamoya disease. Since then, tremendous progress in genomic, transcriptomic, and epigenetic profiling (e.g., methylation profiling) has resulted in new concepts for classifying moyamoya disease. Our literature survey revealed that the pathogenesis involves aberrations of multiple signaling pathways through genetic mutations and altered gene expression. CONCLUSION: We analyzed the content vectors in abstracts using AI, and reviewed the pathophysiology, clinical background, radiological features, treatments, and genetic peculiarity of moyamoya disease.

Deep learning for the diagnosis of mesial temporal lobe epilepsy.

OBJECTIVE: This study aimed to enable the automatic detection of the hippocampus and diagnose mesial temporal lobe epilepsy (MTLE) with the hippocampus as the epileptogenic area using artificial intelligence (AI). We compared the diagnostic accuracies of AI and neurosurgical physicians for MTLE with the hippocampus as the epileptogenic area. METHOD: In this study, we used an AI program to diagnose MTLE. The image sets were processed using a code written in Python 3.7.4. and analyzed using Open Computer Vision 4.5.1. The deep learning model, which was a fine-tuned VGG16 model, consisted of several layers. The diagnostic accuracies of AI and board-certified neurosurgeons were compared. RESULTS: AI detected the hippocampi automatically and diagnosed MTLE with the hippocampus as the epileptogenic area on both T2-weighted imaging (T2WI) and fluid-attenuated inversion recovery (FLAIR) images. The diagnostic accuracies of AI based on T2WI and FLAIR data were 99% and 89%, respectively, and those of neurosurgeons based on T2WI and FLAIR data were 94% and 95%, respectively. The diagnostic accuracy of AI was statistically higher than that of board-certified neurosurgeons based on T2WI data (p = 0.00129). CONCLUSION: The deep learning-based AI program is highly accurate and can diagnose MTLE better than some board-certified neurosurgeons. AI can maintain a certain level of output accuracy and can be a reliable assistant to doctors.

A Case of Perimesencephalic Subarachnoid Hemorrhage with Cerebral Venous Sinus Thrombosis due to Stenosis of the Junction of the Vein of Galen and Rectus Sinus.

Perimesencephalic subarachnoid hemorrhage (PMSAH) is a group of diseases characterized by bleeding around the brainstem. In recent years, it has been suggested that PMSAH is associated with the venous system. We report a case of PMSAH caused by stenosis of the junction of the vein of Galen (VG) and the rectus sinus (RS). A 39-year-old man presented with restlessness at work. He was administered diazepam, and a few minutes later, his consciousness became clear. Imaging showed subarachnoid hemorrhage (SAH) around the right midbrain, occlusion of the right sigmoid sinus and the right transverse sinus, stenosis of the junction of the VG and RS, T2 hyperintensity in the right middle temporal gyrus, and obstruction of the right vein of Labbe. The location of the SAH coincided with stenosis of the junction of the VG and RS. PMSAH was diagnosed due to the increase in intracranial venous pressure caused by the patient's sinus obstruction in addition to the stenosis of the junction of the VG and RS. It is necessary to pay attention to venous return when PMSAH is observed.

Comparison of Thresholds between Bipolar and Monopolar Electrical Cortical Stimulation.

Electrical cortical stimulation is widely performed and is the gold standard for functional mapping in intractable epilepsy patients; however, a standard protocol has not yet been established. With respect to stimulation methods, two techniques can be applied: monopolar and bipolar stimulation. We compared the threshold to induce clinical symptoms between these two stimulation techniques. Twenty patients with intractable epilepsy who underwent electrical cortical stimulation for functional mapping were retrospectively investigated. We evaluated the stimulation intensity thresholds required to induce motor, sensory, and language symptoms. A total of 114 electrodes in 20 patients were used to investigate motor, sensory, and language symptoms. The thresholds required to induce motor (median value, bipolar: 4 mA, monopolar: 5 mA, p < 0.05) and language symptoms (bipolar: 8 mA, monopolar: 10 mA, p < 0.0005) were significantly higher for monopolar stimulation than those for bipolar stimulation. However, for sensory symptoms, no significant differences were found in the required thresholds between monopolar and bipolar stimulation (bipolar: 4 mA, monopolar: 4 mA, p = 0.474). Bipolar cortical stimulation required lower intensities to produce clinical motor and language symptoms and thus would be safe and suitable for screening of the eloquent area in functional mapping.

COVID-19 infection in Hokkaido, Japan might depend on the viscosity of atmospheric air.

BACKGROUND: The large number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has plunged the world into fear in recent times. In Japan, 18,769 novel coronavirus disease 2019 (COVID-19) cases have been reported as of June 30, 2020. This study aimed to assess whether cluster infection prevention is possible by evaluating the association between viral transmission and meteorological factors. METHODS: This study included 1263 people who were successively diagnosed with COVID-19 in Hokkaido, Japan between January 24, 2020 and June 30, 2020. After obtaining the values from the Japanese Meteorological Agency, the average scores of air temperature and humidity were calculated and compared with COVID-19 reproduction numbers, and the association between COVID-19 incidence or reproduction number and meteorological factors was assessed. RESULTS: The COVID-19 reproduction number in Hokkaido had three peaks that came several days before the surge in COVID-19 cases. The peaks are indicative of cluster infections. There was a strong negative correlation between the kinematic viscosity of atmospheric air and the reproduction number. DISCUSSION AND CONCLUSION: Analysis of the reproduction number is important for predicting or suppressing COVID-19 infection clusters. The authors found a strong association between meteorological factors, such as kinematic viscosity of atmospheric air and the incidence of COVID-19 infection. Meteorological forecasts could provide foreknowledge about COVID-19 infection clusters in the future.