Citations

This JAMA Insights explores the management of severe refractory asthma, including medications, access to specialists, and managing environmental triggers.

In this video, the authors present the microsurgical treatment of a superior petrosal dural arteriovenous fistula (dAVF) associated with a symptomatic hemorrhagic brainstem cavernous malformation in a 38-year-old male. The patient underwent a retrosigmoid craniotomy for cavernoma evacuation and fistula disconnection. Intraoperative indocyanine green angiography revealed cortical venous reflux not evident on catheter angiography, prompting reclassification to a higher-grade fistula. Postoperatively, the patient returned to neurological baseline, and follow-up imaging confirmed complete fistula obliteration, cavernoma resection, and new appearance of a developmental venous anomaly (DVA). This case highlights the dynamic nature of dAVF venous drainage and illustrates the rare co-occurrence of dAVF, DVA, and brainstem cavernoma. The video can be found here: https://stream.cadmore.media/r10.3171/2025.7.FOCVID2553

Background: Timely intervention and effective control of Alzheimer’s disease (AD) have been shown to limit memory loss and preserve cognitive function and the ability to perform simple activities in older adults. In addition, magnetic resonance imaging (MRI) scans are one of the most common and effective methods for early detection of AD. With the rapid development of deep learning (DL) algorithms, AD detection based on deep learning has wide applications. Methods: In this research, we have developed an AD detection method based on three-dimensional (3D) convolutional neural networks (CNNs) for 3D MRI images, which can achieve strong accuracy when compared with traditional 3D CNN models. The proposed model has four main blocks, and the multi-layer fusion functionality of each block was used to improve the efficiency of the proposed model. The performance of the proposed model was compared with three different pre-trained 3D CNN architectures (i.e., 3D ResNet-18, 3D InceptionResNet-v2, and 3D Efficientnet-b2) in both tasks of multi-/binary-class classification of AD. Results: Our model achieved impressive classification results of 91.4% for binary-class as well as 80.6% for multi-class classification on the Open Access Series of Imaging Studies (OASIS) database. Conclusions: Such results serve to demonstrate that multi-stage feature fusion of 3D CNN is an effective solution to improve the accuracy of diagnosis of AD with 3D MRI, thus enabling earlier and more accurate diagnosis.

ABSTRACT Cervical leiomyomas are rare pelvic tumors, with interstitial subtypes being the least common. This case highlights a 44‐year‐old female with a 14‐mm interstitial cervical leiomyoma, successfully treated with myomectomy. This approach preserved fertility, demonstrating its feasibility as an alternative to hysterectomy for select patients.

Abstract Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. 2021; Gilson et al. 2023; Almeida et al. 2024; Krishna et al. 2024). These advanced algorithms have the potential to improve disease detection, diagnosis, prognosis, and treatment outcomes. However, the complexity of machine learning models, the large amounts of curated and annotated data required by some methods, and the potential for bias and error make it challenging for individuals to safely and effectively leverage these methods (Lin et al. 2024; Guo et al. 2024; Xu et al. 2024; Linguraru et al. 2024; Wood et al. 2019). To address these challenges, the American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) have worked together to develop a syllabus detailing a recommended set of competencies for medical imaging professionals interacting with these systems. This guide is aimed at four different personas: users of AI systems, purchasers of AI systems, individuals who provide clinical expertise during the development of AI systems (“clinical collaborators”), and developers of AI systems.1 This is a syllabus, not a curriculum, and is intentional in this scope. Recognizing that individuals may benefit from different presentations of the same material, this work enumerates a series of relevant competencies but does not prescribe, nor offer, a method of instruction (Schuur, Rezazade Mehrizi, and Ranschaert 2021; Garin et al. 2023). By addressing the task‐specific demands of each role, this guide will enable medical imaging professionals to utilize machine learning systems more safely and effectively, ultimately improving patient care and outcomes.

Objectives The purpose of the current study was to evaluate differences in total sleep time and heart rate variability (HRV) in active-duty firefighters between on-shift and off-shift days. Methods 59 structural firefighters (age: 37.3±7.3 years; height: 1.80±0.08 m; weight: 88.9±14.1 kg and body mass index (BMI): 27.5±4.1 kg/m2) participated in this longitudinal observation study. Each morning for 15 weeks, firefighters were asked to sync a smart ring with a smartphone application to download night-time data, which were then extracted to a cloud-based software application for later analysis. The software also computed a readiness score each morning. All day types were coded as on-shift or off-shift. Results The average nightly sleep time for all firefighters was 6.95±1.24 hours. When off-shift, firefighters recorded more sleep compared with on-shift nights (off-shift: 6.97±0.50 hours vs on-shift: 6.68±0.52 hours; p<0.0001). HRV (p<0.0001) and readiness scores (p=0.02) were lower off-shift compared with on-shift. For the lagged correlation analysis, total sleep time was moderately positively correlated with the following day’s readiness score. Conclusions Firefighters recorded more total sleep time when off-shift compared with on-shift; however, HRV and readiness scores were higher on-shift. The lag-time correlations indicate an association between sleep time and HRV or readiness scores, with approximately half of the variability in readiness being attributable to changes in total sleep time, which highlights the complexity of the readiness and HRV signals/systems.