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.

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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.

Cardiotoxicity is a critical complication of allogeneic hematopoietic cell transplantation (allo-HCT). In particular, management of severe cardiotoxicity occurring in the early phases of allo-HCT is challenging. We encountered a case of severe cardiotoxicity resulting from AHF six days after allo-HCT, which resisted catecholamines and diuretics. The patient was treated with anthracycline-containing regimens and underwent myeloablative conditioning, including high-dose cyclophosphamide. As invasive circulatory assisting devices were contraindicated because of his immunocompromised status and bleeding tendency, we successfully treated the patient with ivabradine-containing medications. Ivabradine may therefore be considered an alternative drug for the treatment of severe cardiotoxicity induced by cytotoxic agents.

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