Background: Tracheal intubation is the gold standard for securing the airway, and it is not uncommon to encounter intubation difficulties in intensive care units and emergency rooms. Currently, there is a need for an objective measure to assess intubation difficulties in emergency situations by physicians, residents, and paramedics who are unfamiliar with tracheal intubation. Artificial intelligence (AI) is currently used in medical imaging owing to advanced performance. We aimed to create an AI model to classify intubation difficulties from the patient’s facial image using a convolutional neural network (CNN), which links the facial image with the actual difficulty of intubation. Methods: Patients scheduled for surgery at Yamagata University Hospital between April and August 2020 were enrolled. Patients who underwent surgery with altered facial appearance, surgery with altered range of motion in the neck, or intubation performed by a physician with less than 3 years of anesthesia experience were excluded. Sixteen different facial images were obtained from the patients since the day after surgery. All images were judged as “Easy”/“Difficult” by an anesthesiologist, and an AI classification model was created using deep learning by linking the patient’s facial image and the intubation difficulty. Receiver operating characteristic curves of actual intubation difficulty and AI model were developed, and sensitivity, specificity, and area under the curve (AUC) were calculated; median AUC was used as the result. Class activation heat maps were used to visualize how the AI model classifies intubation difficulties. Results: The best AI model for classifying intubation difficulties from 16 different images was generated in the supine-side-closed mouth-base position. The accuracy was 80.5%; sensitivity, 81.8%; specificity, 83.3%; AUC, 0.864; and 95% confidence interval, [0.731-0.969], indicating that the class activation heat map was concentrated around the neck regardless of the background; the AI model recognized facial contours and identified intubation difficulties. Conclusion: This is the first study to apply deep learning (CNN) to classify intubation difficulties using an AI model. We could create an AI model with an AUC of 0.864. Our AI model may be useful for tracheal intubation performed by inexperienced medical staff in emergency situations or under general anesthesia.

Background: This study used an epidural anesthesia practice kit (model) to evaluate the accuracy of epidural anesthesia using standard techniques (blind) and augmented/mixed reality technology and whether visualization using augmented/mixed reality technology would facilitate epidural anesthesia. Methods: This study was conducted at the Yamagata University Hospital (Yamagata, Japan) between February and June 2022. Thirty medical students with no experience in epidural anesthesia were randomly divided into augmented reality (-), augmented reality (+), and semi-augmented reality groups, with 10 students in each group. Epidural anesthesia was performed using the paramedian approach with an epidural anesthesia practice kit. The augmented reality (-) group performed epidural anesthesia without HoloLens2Ⓡ and the augmented reality (+) group with HoloLens2Ⓡ. The semi-augmented reality group performed epidural anesthesia without HoloLens2Ⓡ after 30 s of image construction of the spine using HoloLens2Ⓡ. The epidural space puncture point distance between the ideal insertion needle and participant’s insertion needle was compared. Results: Four medical students in the augmented reality (-), zero in the augmented reality (+), and one in the semi-augmented reality groups failed to insert the needle into the epidural space. The epidural space puncture point distance for the augmented reality (-), augmented reality (+), and semi-augmented reality groups were 8.7 (5.7–14.3) mm, 3.5 (1.8–8.0) mm (P = 0.017), and 4.9 (3.2–5.9) mm (P = 0.027), respectively; a significant difference was observed between the two groups. Conclusions: Augmented/mixed reality technology has the potential to contribute significantly to the improvement of epidural anesthesia techniques.

Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson’s disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person’s data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence.

We aimed to capture the fluctuations in the dynamics of body positions and find the characteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson’s disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at 27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and 92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fourier transforms, the slopes (the representatives of fractality) were obtained from the graph plotting the power spectral density against the frequency in log–log coordinates. Differences in the average slopes were tested by one-way ANOVA and multiple comparisons between every two groups. A decrease in the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthy variations. Significant differences in the patient groups and controls were found in all body positions, where patients always showed smaller absolute values. Our system could measure the whole body’s movement and temporal variations during walking. The impaired fluctuations of body movement in the upper and lower body may contribute to gait and balance disorders in patients.

Hot springs, hot-water bathing, and saunas are widely practiced forms of acute heat exposure and are often perceived as health-promoting. However, emergency clinicians frequently encounter patients in whom these exposures precipitate syncope, hypotension, drowning/aspiration, heat-related illness, and renal or electrolyte disturbances. This narrative review integrates these modalities within a unified “acute heat exposure” framework and summarizes pathophysiology and clinical implications from an emergency medicine perspective. We searched PubMed/MEDLINE from inception to January 2026 using controlled vocabulary and free-text terms related to heat stress, thermoregulation, hot-water immersion, sauna exposure, and acute clinical outcomes; evidence was synthesized qualitatively. Across modalities, acute heat exposure induces shared physiological responses—peripheral vasodilation, relative hypovolemia, circulatory stress, and internal heat storage—that can trigger diverse emergency presentations. We classify acute heat exposure–related illness into four domains: (1) cardiovascular events, including syncope, hypotension, and arrhythmic/ischemic complications in vulnerable individuals; (2) the heat-illness spectrum from exhaustion to heat stroke with organ dysfunction; (3) renal and electrolyte disturbances related to dehydration and hypoperfusion; and (4) neurological and traumatic complications, including falls, drowning, and aspiration. This framework may support risk stratification, evaluation, management, and prevention after hot spring, hot bath, or sauna use.

Summary: Blood transfusion is one of the causes of anaphylaxis during general anaesthesia. In most cases, adrenaline administration quickly alleviates circulatory failure and its continuous administration for days is very rare. We present the case of a 79‐year‐old man who experienced prolonged anaphylactic circulatory failure with increased histamine and tryptase concentrations following laparoscopic total gastrectomy, during which he received a blood transfusion. He developed hypotension soon after the initiation of blood transfusion during surgery. We immediately administered intravenous adrenaline, followed by a continuous adrenaline infusion to stabilise the circulation. Circulatory failure recurred and itchy wheals appeared when the adrenaline dose was reduced necessitating continuous adrenaline administration for > 24 h. The histamine and tryptase serum concentrations increased when the adrenaline dose was reduced. The timing of anaphylaxis onset and skin test results excluded rocuronium, cefazolin, ropivacaine and propofol as causes of the anaphylaxis. Therefore, the blood transfusion was the most likely cause. For anaphylaxis due to common causes, boluses of adrenaline often restore the circulation shortly after onset. However, transfusion‐induced anaphylaxis, as in this case, can require prolonged infusion of adrenaline because of the lack of allergen clearance. For such cases, careful follow‐up is very important.