Noncompressible truncal hemorrhage is a leading cause of preventable death in military prehospital settings, particularly in combat environments where advanced imaging is unavailable. The Focused Assessment with Sonography in Trauma (FAST) exam is critical for diagnosing intra-abdominal bleeding. However, Army medics typically lack formal ultrasound training. This study examines whether artificial intelligence (AI) assistance can enhance medics' proficiency in performing FAST exams, thereby improving the speed and accuracy of trauma triage in austere conditions. This is a prospective, randomized controlled trial that involved 60 Army medics who performed 3-view abdominal FAST exams, both with and without AI assistance, using the EchoNous Kosmos device. Investigators randomized participants into 2 groups and evaluated based on time to completion, adequacy of imaging, and confidence in using the device. Two trained investigators assessed adequacy and the participants reported confidence in the device using a 5-point Likert scale. We then analyzed data using the t-test for parametric data, the Wilcoxon rank-sum test, and Cohen's Kappa test for interrater reliability. The AI-assisted group completed the FAST exam in an average of 142.57 seconds compared to 143.87 seconds (P = .9) for the non-AI-assisted group, demonstrating no statistically significant difference in time. However, the AI-assisted group demonstrated significantly higher adequacy in the left upper quadrant and pelvic views (P = .008 and P = .004, respectively). Participants reported significantly higher confidence in the AI-assisted group, with a median score of 4.00 versus 2.50 (P = .006). Interrater agreement was moderate to substantial, with Cohen's Kappa values indicating significant reliability. AI assistance did not significantly reduce the time required to complete a FAST exam but improved image adequacy and user confidence. These findings suggest that AI tools can enhance the quality of FAST exams conducted by minimally trained medics in combat settings. Further research is needed to explore integrating AI-assisted ultrasound training in military medic curricula to optimize trauma care in austere environments.

Delta biomarkers that reflect changes in tumour burden over time can support personalised follow-up in head and neck cancer. However, their clinical use can be limited by the need for manual image segmentation. This study externally evaluates a deep learning model for automatic determination of volume change from serial 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) scans to stratify patients by loco-regional outcome. Patient/material and methods: An externally developed deep learning algorithm for tumour segmentation was applied to pre- and post-radiotherapy (RT, with or without concomitant chemoradiotherapy) PET/CT scans of 50 consecutive head and neck cancer patients from The Christie NHS Foundation Trust, UK. The model, originally trained on pre-treatment scans from a different institution, was deployed to derive tumour volumes at both time points. The AI-derived change in tumour volume (ΔPET-Gross tumour volume (GTV)) was calculated for each patient. Kaplan-Meier analysis assessed loco-regional control based on ΔPET-GTV, dichotomised at the cohort median. In a separate secondary analysis confined to the pre‑treatment scans, a radiation oncologist qualitatively evaluated the AI‑generated PET‑GTV contours. Patients with higher ΔPET-GTV (i.e. greater tumour shrinkage) had significantly improved loco-regional control (log-rank p = 0.02). At 2 years, control was 94.1% (95% CI: 83.6-100%) vs. 53.6% (95% CI: 32.2-89.1%). Only one of nine failures occurred in the high ΔPET-GTV group. Clinician review found AI volumes acceptable for planning in 78% of cases. In two cases, the algorithm identified oropharyngeal primaries on pre-treatment PET-CT before clinical identification. Deep learning-derived ΔPET-GTV may support clinically meaningful assessment of post-treatment disease status and risk stratification, offering a scalable alternative to manual segmentation in PET/CT follow-up.

Body composition (BC) analysis is performed to quantify the relative amounts of different body tissues as a measure of physical fitness and tumor cachexia. We hypothesized that relative changes in body composition (BC) parameters, assessed by an artificial intelligence-based, PACS-integrated software, between baseline imaging before the start of radioligand therapy (RLT) and interim staging after two RLT cycles could predict overall survival (OS) in patients with metastatic castration-resistant prostate cancer. We conducted a single-center, retrospective analysis of 92 patients with mCRPC undergoing [¹⁷⁷Lu]Lu-PSMA RLT between September 2015 and December 2023. All patients had [⁶⁸ Ga]Ga-PSMA-11 PET/CT at baseline (≤ 6 weeks before the first RLT cycle) and at interim staging (6-8 weeks after the second RLT cycle) allowing for longitudinal BC assessment. During follow-up, 78 patients (85%) died. Median OS was 16.3 months. Median follow-up time in survivors was 25.6 months. The 1 year mortality rate was 32.6% (95%CI 23.0-42.2%) and the 5 year mortality rate was 92.9% (95%CI 85.8-100.0%). In multivariable regression, relative change in visceral adipose tissue (VAT) (HR: 0.26; p = 0.006), previous chemotherapy of any type (HR: 2.4; p = 0.003), the presence of liver metastases (HR: 2.4; p = 0.018) and a higher baseline De Ritis ratio (HR: 1.4; p < 0.001) remained independent predictors of OS. Patients with a higher decrease in VAT (< -20%) had a median OS of 10.2 months versus 18.5 months in patients with a lower VAT decrease or VAT increase (≥ -20%) (log-rank test: p = 0.008). In a separate Cox model, the change in VAT predicted OS (p = 0.005) independent of the best PSA response after 1-2 RLT cycles (p = 0.09), and there was no interaction between the two (p = 0.09). PACS-Integrated, AI-based BC monitoring detects relative changes in the VAT, Which was an independent predictor of shorter OS in our population of patients undergoing RLT.

Inflammation plays important roles in the pathogenesis of vascular diseases. We here show the involvement of perivascular inflammation in aortic dilatation of Marfan syndrome (MFS). In the aorta of MFS patients and Fbn1C1041G/+ mice, macrophages markedly accumulated in periaortic tissues with increased inflammatory cytokine expression. Metabolic inflammatory stress induced by a high-fat diet (HFD) enhanced vascular inflammation predominantly in periaortic tissues and accelerated aortic dilatation in Fbn1C1041G/+ mice, both of which were inhibited by low-dose pitavastatin. HFD feeding also intensifies structural disorganization of the tunica media in Fbn1C1041G/+ mice, including elastic fiber fragmentation, fibrosis, and proteoglycan accumulation, along with increased activation of TGF-β downstream targets. Pitavastatin treatment mitigated these alterations. For non-invasive assessment of PVAT inflammation in a clinical setting, we developed an automated analysis program for CT images using machine learning techniques to calculate the perivascular fat attenuation index of the ascending aorta (AA-FAI), correlating with periaortic fat inflammation. The AA-FAI was significantly higher in patients with MFS compared to patients without hereditary connective tissue disorders. These results suggest that perivascular inflammation contributes to aneurysm formation in MFS and might be a potential target for preventing and treating vascular events in MFS.

Radiology in Canada is evolving through a combination of clinical innovation, collaborative research and the adoption of advanced imaging technologies. This overview highlights contributions from selected academic centres across the country that are shaping diagnostic and interventional practice. At Dalhousie University, researchers have led efforts to improve contrast media safety, refine imaging techniques for hepatopancreatobiliary diseases, and develop peer learning programs that support continuous quality improvement. The University of Ottawa has made advances in radiomics, magnetic resonance imaging protocols, and virtual reality applications for surgical planning, while contributing to global research networks focused on evaluating LI-RADS performance. At the University of British Columbia, the implementation of photon-counting CT, dual-energy CT, and artificial intelligence tools is enhancing diagnostic precision in oncology, trauma, and stroke imaging. The Hospital for Sick Children is a leader in paediatric radiology, with work ranging from artificial intelligence (AI) brain tumour classification to innovations in foetal MRI and congenital heart disease imaging. Together, these initiatives reflect the strength and diversity of Canadian radiology, demonstrating a shared commitment to advancing patient care through innovation, data-driven practice and collaboration.

Dual-energy X-ray absorptiometry (DXA) is the standard method for assessing areal bone mineral density (aBMD), diagnosing osteoporosis, and predicting fracture risk. However, DXA's availability is limited in resource-poor areas. This study aimed to develop an artificial intelligence (AI) system capable of estimating aBMD from standard radiographs. The study was part of the Vietnam Osteoporosis Study, a prospective population-based research involving 3783 participants aged 18 years and older. A total of 7060 digital radiographs of the frontal pelvis and lateral spine were taken using the FCR Capsula XLII system (Fujifilm Corp., Tokyo, Japan). aBMD at the femoral neck and lumbar spine was measured with DXA (Hologic Horizon, Hologic Corp., Bedford, MA, USA). An ensemble of seven deep-learning models was used to analyze the X-rays and predict bone mineral density, termed "xBMD". The correlation between xBMD and aBMD was evaluated using Pearson's correlation coefficients. The correlation between xBMD and aBMD at the femoral neck was strong ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.90; 95% CI, 0.88-0.91), and similarly high at the lumbar spine ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.87; 95% CI, 0.85-0.88). This correlation remained consistent across different age groups and genders. The AI system demonstrated excellent performance in identifying individuals at high risk for hip fractures, with area under the ROC curve (AUC) values of 0.96 (95% CI, 0.95-0.98) at the femoral neck and 0.97 (95% CI, 0.96-0.99) at the lumbar spine. These findings indicate that AI can accurately predict aBMD and identify individuals at high risk of fractures. This AI system could provide an efficient alternative to DXA for osteoporosis screening in settings with limited resources and high patient demand. An AI system developed to predict aBMD from X-rays showed strong correlations with DXA ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> = 0.90 at femoral neck; =  <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>r</mi></math> 0.87 at lumbar spine) and high accuracy in identifying individuals at high risk for fractures (AUC = 0.96 at femoral neck; AUC = 0.97 at lumbar spine).

BackgroundSevere multiple sclerosis (MS) presents challenges for clinical research due to mobility constraints and specialized care needs. Traditional MRI studies often exclude this population, limiting understanding of severe MS progression. Portable, ultra-low-field MRI enables bedside imaging. ObjectivesTo (i) assess the feasibility of portable MRI in severe MS, (ii) compare measurement approaches for automated tissue volumetry from ultra-low-field MRI. MethodsThis prospective study enrolled 40 progressive MS patients (24 severely disabled, 16 less severe) from academic and skilled nursing settings. Participants underwent 0.064T MRI for tissue volumetry using conventional and artificial intelligence (AI)-driven segmentation. Clinical assessments included physical disability and cognition. Group comparisons and MRI-clinical associations were assessed. ResultsMRI passed rigorous quality control, reflecting complete brain coverage and lack of motion artifact, in 38/40 participants. In terms of severe versus less severe disease, the largest effect sizes were obtained with conventionally-calculated gray matter (GM) volume (partial 2=0.360), cortical GM volume (partial 2=0.349), and whole brain volume (partial 2=0.290) while an AI-based approach yielded the highest effect size for white matter volume (partial 2=0.209). For clinical outcomes, the most consistent associations were found using conventional processing while AI-based methods were dependent on algorithm and input image, especially for cortical GM volume. ConclusionPortable, ultralow-field MRI is a feasible bedside tool that can provide insights into late-stage neurodegeneration in individuals living with severe MS. However, careful consideration is required in implementing tissue volumetry pipelines as findings are heavily dependent on the choice of algorithm and input.

Most acute coronary syndromes (ACS) originate from coronary plaques that are angiographically mild and not flow limiting. These lesions, often characterised by thin-cap fibroatheroma, large lipid cores and macrophage infiltration, are termed 'vulnerable plaques' and are associated with a heightened risk of future major adverse cardiovascular events (MACE). However, current imaging modalities lack robust predictive power, and treatment strategies for such plaques remain controversial. The PREDICT-AI study aims to develop and externally validate a machine learning (ML)-based risk score that integrates optical coherence tomography (OCT) plaque features and patient-level clinical data to predict the natural history of non-flow-limiting coronary lesions not treated with percutaneous coronary intervention (PCI). This is a multicentre, prospective, observational study enrolling 500 patients with recent ACS who undergo comprehensive three-vessel OCT imaging. Lesions not treated with PCI will be characterised using artificial intelligence (AI)-based plaque analysis (OctPlus software), including quantification of fibrous cap thickness, lipid arc, macrophage presence and other microstructural features. A three-step ML pipeline will be used to derive and validate a risk score predicting MACE at follow-up. Outcomes will be adjudicated blinded to OCT findings. The primary endpoint is MACE (composite of cardiovascular death, myocardial infarction, urgent revascularisation or target vessel revascularisation). Event prediction will be assessed at both the patient level and plaque level. The PREDICT-AI study will generate a clinically applicable, AI-driven risk stratification tool based on high-resolution intracoronary imaging. By identifying high-risk, non-obstructive coronary plaques, this model may enhance personalised management strategies and support the transition towards precision medicine in coronary artery disease.

<b>Objective:</b> To compare the diagnostic performance of artificial intelligence-assisted automated breast ultrasound (AI-ABUS) with traditional handheld ultrasound (HHUS) in breast cancer screening. <b>Methods:</b> A total of 36 171 women undergoing breast cancer ultrasound screening in Futian District, Shenzhen, between July 1, 2023 and June 30, 2024 were prospectively recruited and assigned to either the AI-ABUS or HHUS group based on the screening modality used. In the AI-ABUS group, image acquisition was performed on-site by technicians, and two ultrasound physicians conducted remote diagnoses with AI assistance, supported by a follow-up management system. In the HHUS group, one ultrasound physician conducted both image acquisition and diagnosis on-site, and follow-up was led by clinical physicians. Based on the reported malignancy rates of different BI-RADS categories, the number of undiagnosed breast cancer cases in individuals without pathology was estimated, and adjusted detection rates were calculated. Primary outcomes included screening positive rate, biopsy rate, cancer detection rate, loss-to-follow-up rate, specificity, and sensitivity. <b>Results:</b> The median age [interquartile range, <i>M</i> (<i>Q</i>₁, <i>Q</i>₃)] of the 36 171 women was 43.8 (36.6, 50.8) years. A total of 14 766 women (40.82%) were screened with AI-ABUS and 21 405 (59.18%) with HHUS. Baseline characteristics showed no significant differences between the groups (all <i>P</i>>0.05). The AI-ABUS group had a lower screening positive rate [0.59% (87/14 766) vs 1.94% (416/21 405)], but higher biopsy rate [47.13% (41/87) vs 16.10% (67/416)], higher cancer detection rate [1.69‰ (25/14 766) vs 0.47‰ (10/21 428)], and lower loss-to-follow-up rate (6.90% vs 71.39%) compared to the HHUS group (all <i>P</i><0.05). There was no statistically significant difference in the distribution of breast cancer pathological stages among those who underwent biopsy between the two groups (<i>P</i>>0.05). The specificity of AI-ABUS was higher than that of HHUS [89.77% (13, 231/14 739) vs 74.12% (15, 858/21 394), <i>P</i><0.05], while sensitivity did not differ significantly [92.59% (25/27) vs 90.91% (10/11), <i>P</i>>0.05]. After estimating undiagnosed cancer cases among participants without pathology, the adjusted detection rate was 2.30‰ (34/14 766) in the AI-ABUS group and ranged from 1.17‰ to 2.75‰ [(25-59)/21 428] in the HHUS group. In the minimum estimation scenario, the detection rate in the AI-ABUS group was significantly higher (<i>P</i><0.05); in the maximum estimation scenario, the difference was not statistically significant (<i>P</i>>0.05). <b>Conclusions:</b> The AI-ABUS model, combined with an intelligent follow-up management system, enables a higher breast cancer detection rate with a lower screening positive rate, improved specificity, and reduced loss to follow-up. This suggests AI-ABUS is a promising alternative model for breast cancer screening.

Dental implants have become more straightforward and convenient with advancements of digital technology in dentistry. Implant planning utilizing artificial intelligence (AI) has been attempted, yet its clinical efficacy remains underexplored. We aimed to assess the clinical applicability of AI-based implant planning software as a decision-support tool in comparison with those placed by clinicians which were clinically appropriate in their three-dimensional positions. Overall, 350 implants from 228 patients treated at four university hospitals were analyzed. The AI algorithm was developed using enhanced deep convolutional neural networks. Implant positions planned by the AI were compared with those placed freehand by clinicians. Three-dimensional deviations were measured and analyzed according to clinical factors, including the presence of opposing or contralateral teeth, jaw, and side. Independent sample t-test and two-way ANOVA were employed for statistical analysis. The mean coronal, apical, and angular deviations were 2.99 ± 1.56 mm, 3.66 ± 1.68 mm, and 7.56 ± 4.67°, respectively. Angular deviation was significantly greater in the absence of contralateral teeth (p=0.039), and apical deviation was significantly greater in the mandible (p<0.001). The AI-based 3D implant planning tool demonstrated potential as a decision-support system by providing valuable guidance in clinical scenarios. However, discrepancies between AI-generated and actual implant positions indicate that further research and development are needed to enhance its predictive accuracy. AI-based implant planning may serve as a supportive tool under clinician supervision, potentially improving workflow efficiency and contributing to more standardized implant treatment planning as the technology advances.