Citations

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.

Abstract The acetabular labrum is a fibrocartilaginous structure that deepens the acetabular socket and helps maintain a suction seal. In patients with hip dysplasia, a distinct labral tear pattern called an inside‐out labral tear is often seen. This tear pattern is particularly challenging to correct via conventional techniques since the labrum can potentially remain everted and insufficiently restore the native hip labral suction seal after repair. An inverted hip labral repair technique is performed for this pathology at our institution and focuses on achieving optimal labral orientation to enhance the suction seal and joint stability, particularly in dysplastic hips. This Technical Note aims to describe the key surgical principles and clinical considerations of inverted hip labral repair. Technique Video video See video under supplementary data.

Abstract INTRODUCTION In multicenter Alzheimer's disease studies, amyloid positron emission tomography (PET) visual reads are typically performed centrally by a few experts. Incorporating a broader reader network enhances scalability and generalizability. METHODS Ten neuroimaging experts from eight Alzheimer's Disease Research Centers (ADRCs) visually read 180 amyloid PET scans (30 scans and 15 duplicate scans for each of four tracers, imaged across a wide variety of scanners), using preferred reading software without anatomical imaging or quantitation. Scans were classified as elevated or non‐elevated per tracer‐specific criteria. Inter‐ and intra‐rater agreement was assessed. RESULTS Inter‐rater agreement was substantial (Fleiss’ κ = 0.78), with full consensus on 69% of scans. Inter‐rater reliability was substantial to perfect across tracers (Fleiss’ κ = 0.70–0.87). Intra‐rater agreement was substantial to perfect (Cohen's κ = 0.79‐1). Scans with intermediate (10–40 Centiloid) quantitation had lower reader agreement. DISCUSSION A multicenter expert network achieved substantial agreement classifying amyloid PET scans. These scans provide a standard for reader training and reliability assurance in future studies. Highlights Calibration methods ensure reliable amyloid positron emission tomography (PET) visual reads across multiple raters. Substantial agreement is possible across readers using their preferred tools. Agreement is also substantial regardless of the amyloid PET tracer used. Scans with intermediate (10–40 Centiloid) quantitation have lower reader agreement. The calibration set will become a training tool for amyloid PET visual read studies.

Abstract Background Hospital discharge summaries are critical for ensuring continuity of care, but their quality often varies. Large language models (LLMs) have the potential to standardize and enhance the efficiency of this documentation process. Objectives To evaluate the quality of hospital discharge summaries created by an LLM‐based hospital course drafting tool created by Epic Systems compared with human‐written summaries. Methods Retrospective study at a single tertiary‐care institution in 2024. The cohort included 100 adult hospitalizations lasting >72 h across medical and surgical dismissing services. No interventions were performed. Summaries (LLM‐generated vs. human‐written) were independently reviewed using a standardized rubric covering nine domains (e.g., comprehensiveness, clarity, relevance). Scores were normalized and compared. Readability was assessed using Flesch Reading Ease. Results LLM‐generated summaries outperformed human‐written summaries across all criteria ( p < .05), with the greatest difference observed in comprehensiveness (LLM median 0.62 vs. human −0.23). Human‐written summaries from surgical services scored lower than those from medical services, but LLM performance was consistent across both. Human summaries had higher Flesch Reading Ease scores (33.11 vs. 26.2; p < .05), reflecting simpler language. Conclusions LLM‐generated summaries demonstrated superior quality, consistency, and clinical utility compared with human‐written summaries, highlighting their potential to improve documentation efficiency and standardization.