Citations

Graph neural networks (GNNs) are increasingly used for medical imaging analysis and biological data modeling, where the integration of radiomics, topology, geometry, and generative artificial intelligence (AI) may improve representation learning from medical images and related biomedical data. Across the reviewed literature, GNNs show particular value for modeling spatial relationships, multimodal interactions, graph-structured biological networks, and non-Euclidean imaging features that are difficult to capture using conventional convolutional architectures alone. Topology- and geometry-aware approaches further expand this capability by encoding multi-scale structure, higher-order relationships, curvature, geodesic organization, and equivariant spatial priors. Hybrid graph–transformer models and generative graph methods represent emerging directions for modeling long-range dependencies, augmenting scarce datasets, supporting synthetic pretraining, and improving representation learning in low-label or heterogeneous biomedical settings. However, clinical translation remains limited by variability in graph construction, limited external validation, computational cost, scalability constraints, interpretability challenges, and uncertainty regarding the biological realism of synthetic data. Overall, this review highlights that GNN-based medical imaging analysis is most likely to advance when graph construction is biologically justified, model performance is evaluated across diverse clinical cohorts, and technical gains are paired with transparent validation, interpretability, and implementation strategies.

Abstract Nonmyeloablative-related haploidentical allogeneic stem cell transplantation (NMAC-HID allo-HSCT) has emerged as an additional treatment to achieve durable remission in sickle cell disease (SCD), a prevalent blood disorder characterized by painful vaso-occlusive crises and chronic anemia. The standard of care (SOC) for SCD includes hydroxyurea, pain management, and blood transfusion, but patients with SCD still lose several decades of life expectancy. Gene therapy (GT) for SCD is the other treatment for lifelong disease amelioration in SCD, with accessibility limited by cost and manufacturing capacity in the United States and globally. Two recent prospective studies that evaluated NMAC-HID allo-HSCT validated haploidentical allotransplantation as an efficacious and accessible treatment option in the era of GT. Given the ongoing price negotiation across jurisdictions for GT implementation and the absence of cost-effectiveness data comparing NMAC-HID allo-HSCT and GT, we conducted a cost-effectiveness analysis of NMAC-HID allo-HSCT vs GT vs SOC for adults and children living with SCD. The primary outcomes were the incremental cost-effectiveness ratio and the net monetary benefits across these 3 strategies. The secondary outcome was the maximum cost-effective threshold price for GT compared with NMAC-HID allo-HSCT. Treatment with SOC, NMAC-HID allo-HSCT, and GT accrued 14.3, 20.1, and 22.1 quality-adjusted life-years at costs of $1.22 million, $1.15 million, and $2.75 million, respectively. NMAC-HID allo-HSCT was the cost-effective strategy compared with GT in 100% of 10 000 Monte Carlo iterations across the base case and all scenario analyses. The maximum cost-effective thresholds for GT vs SOC were $1.4 million in the United States and $4200 to $22 000 across India, Nigeria, and Tanzania, depending on willingness-to-pay thresholds.

ABSTRACT Objective Odontogenic sinusitis (ODS) ranges from isolated maxillary disease to pansinusitis. We compared patient characteristics, microbial and histopathological features, and symptom outcomes following endoscopic sinus surgery (ESS) between low and high radiographic burden ODS patients. Methods ODS patients undergoing ESS between 01/2013 and 08/2024 were reviewed. Preoperative CT scans were used to classify patients into high radiographic burden [with ostiomeatal complex (OMC) opacification] and low radiographic burden ODS [clear OMC]. Demographic, microbiological, and histopathological data were analyzed. Symptom improvement was assessed using pre‐ and post‐operative SNOT‐22 scores. Results Eighty‐seven patients were identified (high CT burden: 70; low CT burden: 17). High CT burden ODS was associated with older age ( p = 0.014), higher overall degree of inflammation ( p = 0.002), hyperplastic/papillary epithelial changes ( p = 0.034), and neutrophil infiltration ( p = 0.003). Culture of classic ODS bacteria ( Fusobacterium spp., Prevotella spp., mixed anaerobes, Streptococcus anginosus group) did not correlate with CT disease burden ( p = 0.110). No independent predictors of radiographic high‐burden disease were identified. Post‐ESS, significant reduction in total and rhinologic SNOT‐22 scores was only observed in the radiographic high‐burden ODS group ( n = 39; median reductions: 23 and 12 respectively; p < 0.001). Conclusion High radiographic burden was associated with older patients and with tissue inflammation level. Classic ODS bacterial profile did not associate with increased CT burden. While these findings remain exploratory, the observed differences in inflammatory and symptomatic profiles between groups suggest that radiographic burden warrants further study as a potential factor in ODS clinical decision‐making. Level of Evidence 3.

The spinal cord plays a central role in the pathophysiology and clinical manifestations of multiple sclerosis (MS), yet remains under-studied compared with the brain. This review summarizes key insights from the 2025 North American Imaging in MS Spinal Cord Imaging Workshop, highlighting recent advances, ongoing challenges, and future opportunities in MS spinal cord imaging. We review pathological studies and outline the clinical relevance of spinal cord lesions and atrophy for diagnosis, prognosis, and disease monitoring, highlighting emerging biomarkers of progression independent of relapse activity. Correlations between magnetic resonance imaging, histopathology, and clinical outcomes support the validation and translational potential of advanced spinal cord imaging techniques. Finally, we discuss spinal cord–specific processing pipelines and reproducibility challenges. Collectively, these insights underscore the need to integrate advanced and quantitative spinal cord imaging into clinical trials, research studies, and—when feasible—clinical care, to fully capture the extent of MS pathology, and ultimately improve patient outcomes.

Background: Longitudinal assessments are increasingly common as a component of continuing certification programs offered by medical specialty boards. The goal of this study was to understand anesthesiologists’ utilization pattern, performance, and perception of the longitudinal assessment program administered by the American Board of Anesthesiology (Raleigh, North Carolina). Methods: The authors analyzed assessment usage and survey data from all anesthesiologists who participated in the Maintenance of Certification in Anesthesiology (MOCA) Minute from January 2018 to December 2019. Results: During the 24-month study period, 25,404 anesthesiologists completed all 120 questions assigned in at least 1 calendar year, collectively 5,801,160 responses to 1,646 unique questions. Participants were more likely to complete questions at the end of each quarter, with the highest frequency at the end of the year. Seventy-five percent of responses were first-time attempts, and 17% were first repeats. The median interval between original question presentation and first repeat was 314 days. The mean ± SD percent correct score was 68.3 ± 8.0% for first-time questions and 75.5 ± 12.6% for first repeats. The mean ± SD time to answer a first-time question was 26.8 ± 13.1 s, with shorter times for questions with correct than incorrect responses (25.0 ± 12.7 vs . 30.7 ± 13.3 s; time difference, −5.69 s; 95% CI, −5.71 to −5.66; P < 0.001). Of 5.11 million responses with ratings of relevance (88.2% of all responses analyzed), 4.86 million (95.2%) were felt to be somewhat or very relevant to practice. The percent correct scores were higher for responses with higher relevance and confidence ratings. For responses rated “not relevant,” “somewhat relevant,” and “very relevant,” the percent correct was 58.5%, 67.6%, and 77.0%, respectively; for responses rated as “unsure,” “somewhat confident,” and “very confident,” the percent correct was 53.6%, 67.9%, and 84.4%, respectively. Conclusions: MOCA Minute was generally used as designed. Evidence of learning was suggested by improved performance on repeat questions.

Purpose of review Ophthalmology has emerged as the proving ground for medical artificial intelligence (AI) because of its imaging-centric workflows, standardized data acquisition, and well defined clinical endpoints. Recently, the field has started transitioning from narrowly trained, task-specific algorithms toward multimodal models that are trained on large datasets and can be adapted to many tasks. These models may integrate information from images, text, and clinical data and are increasingly trialed in real-world settings. This review examines recent advances and ongoing controversies in the use of AI as a basic science, translational, and clinical research tool in ophthalmology. Recent findings Recent literature highlights the growing role of self-supervised and foundation models for scalable phenotyping, biomarker discovery, and cross-task generalization, particularly in retinal imaging and optical coherence tomography (OCT). In parallel, large-scale clinical deployments, including cloud-based OCT platforms and autonomous diabetic retinopathy screening, demonstrate clinical impact while revealing persistent challenges related to workflow integration and patient generalizability. Regulatory and ethics-focused studies underscore increasing attention to validation rigor, bias, and postdeployment monitoring, exposing heterogeneity in research standards and physician practices. Summary Ophthalmic AI is maturing toward real-world clinical implementation. Future progress will depend less on performance gains alone and more on clinical alignment, rigorous validation, equity-aware evaluation, and postdeployment surveillance, alongside continued investment in basic science to advance disease understanding and inform robust, generalizable model development.