Objective: To investigate the relationship between weather and headache occurrence using big data from an electronic headache diary smartphone application with recent statistical and deep learning (DL)‐based methods. Background: The relationship between weather and headache occurrence remains unknown. Methods: From a database of 1 million users, data from 4375 users with 336,951 hourly headache events and weather data from December 2020 to November 2021 were analyzed. We developed statistical and DL‐based models to predict the number of hourly headache occurrences mainly from weather factors. Temporal validation was performed using data from December 2019 to November 2020. Apart from the user dataset used in this model development, the physician‐diagnosed headache prevalence was gathered. Results: Of the 40,617 respondents, 15,127/40,617 (37.2%) users experienced physician‐diagnosed migraine, and 2458/40,617 (6.1%) users had physician‐diagnosed non‐migraine headaches. The mean (standard deviation) age of the 4375 filtered users was 34 (11.2) years, and 89.2% were female (3902/4375). Lower barometric pressure ( p < 0.001, gain = 3.9), higher humidity ( p < 0.001, gain = 7.1), more rainfall ( p < 0.001, gain = 3.1), a significant decrease in barometric pressure 6 h before ( p < 0.001, gain = 11.7), higher barometric pressure at 6:00 a.m. on the day ( p < 0.001, gain = 4.6), lower barometric pressure on the next day ( p < 0.001, gain = 6.7), and raw time‐series barometric type I (remaining low around headache attack, p < 0.001, gain = 10.1) and type II (decreasing around headache attack, p < 0.001, gain = 10.1) changes over 6 days, were significantly associated with headache occurrences in both the statistical and DL‐based models. For temporal validation, the root mean squared error (RMSE) was 13.4, and the determination coefficient ( R ² ) was 52.9% for the statistical model. The RMSE was 10.2, and the R ² was 53.7% for the DL‐based model. Conclusions: Using big data, we found that low barometric pressure, barometric pressure changes, higher humidity, and rainfall were associated with an increased number of headache occurrences.

Background: Evaluating muscle mass and function among stroke patients is important. However, evaluating muscle volume and function is not easy due to the disturbances of consciousness and paresis. Temporal muscle thickness (TMT) has been introduced as a novel surrogate marker for muscle mass, function, and nutritional status. We herein performed a narrative literature review on temporal muscle and stroke to understand the current meaning of TMT in clinical stroke practice. Methods: The search was performed in PubMed, last updated in October 2021. Reports on temporal muscle morphomics and stroke-related diseases or clinical entities were collected. Results: Four studies reported on TMT and subarachnoid hemorrhage, two studies on intracerebral hemorrhage, two studies on ischemic stroke, two studies on standard TMT values, and two studies on nutritional status. TMT was reported as a prognostic factor for several diseases, a surrogate marker for skeletal muscle mass, and an indicator of nutritional status. Computed tomography, magnetic resonance imaging, and ultrasonography were used to measure TMT. Conclusions: TMT is gradually being used as a prognostic factor for stroke or a surrogate marker for skeletal muscle mass and nutritional status. The establishment of standard methods to measure TMT and large prospective studies to further investigate the relationship between TMT and diseases are needed.

Background: Misdiagnoses of headache disorders are a serious issue. Therefore, we developed an artificial intelligence-based headache diagnosis model using a large questionnaire database in a specialized headache hospital. Methods: Phase 1: We developed an artificial intelligence model based on a retrospective investigation of 4000 patients (2800 training and 1200 test dataset) diagnosed by headache specialists. Phase 2: The model’s efficacy and accuracy were validated. Five non-headache specialists first diagnosed headaches in 50 patients, who were then re-diagnosed using AI. The ground truth was the diagnosis by headache specialists. The diagnostic performance and concordance rates between headache specialists and non-specialists with or without artificial intelligence were evaluated. Results: Phase 1: The model’s macro-average accuracy, sensitivity (recall), specificity, precision, and F values were 76.25%, 56.26%, 92.16%, 61.24%, and 56.88%, respectively, for the test dataset. Phase 2: Five non-specialists diagnosed headaches without artificial intelligence with 46% overall accuracy and 0.212 kappa for the ground truth. The statistically improved values with artificial intelligence were 83.20% and 0.678, respectively. Other diagnostic indexes were also improved. Conclusions: Artificial intelligence improved the non-specialist diagnostic performance. Given the model’s limitations based on the data from a single center and the low diagnostic accuracy for secondary headaches, further data collection and validation are needed.