Ultrasound (US) imaging is the primary choice for diagnosing and triaging patients in the battlefield as well as emergency medicine due to ease of portability and low-power requirements. Interpretation and acquisition of ultrasound images can be challenging and requires personnel with specialized training. Incorporating artificial intelligence (AI) can enhance the imaging process while improving diagnostic accuracy. To accomplish this goal, we have developed a full torso tissue-mimicking phantom for simulating US image capture at each site of the extended-focused assessment with sonography for trauma (eFAST) exam and is suitable for developing AI guidance and classification models. The US images taken from the phantom were used to train AI models for detection of specific anatomical features and injury state diagnosis. The tissue-mimicking phantom successfully simulated full thoracic motion as well as modular injuries at each scan site. AI models trained from the tissue phantom were able to achieve IOU's greater than 0.80 and accuracy of 71.5% on blind inferences. In summary, the tissue mimicking phantom is a reliable tool for acquiring eFAST images for training AI models. Furthermore, the tissue phantom could be implemented for training personnel on ultrasound examination techniques as well as developing image acquisition automation techniques.

This review explores the role of artificial intelligence (AI) in visceral adipose tissue (VAT) and ectopic fat imaging. It aims to evaluate how AI may be used to enhance the efficiency and accuracy of cardiovascular disease (CVD) risk assessment. It addresses key questions regarding AI's capabilities in risk prediction, segmentation, and integration with large volume data for CVD risk assessment. Recent studies demonstrate that AI, powered by deep learning models, significantly improve VAT and ectopic fat segmentation. AI can also be used to facilitate early detection of cardiometabolic risks and allows integration of imaging with clinical data for a more personalized approach to medicine. Emerging applications include AI-enabled telehealth and continuous monitoring through wearable technologies. AI is transforming VAT and ectopic fat imaging by enabling more precise, personalized, and scalable assessments of fat distribution and cardiovascular risk. While challenges remain, such as model interpretability, future research will likely focus on refining algorithms and expanding AI's clinical applications, potentially redefining obesity and CVD risk management.

Imaging triage of stroke patients is primarily based on perfusion imaging. Simplified triage based on non-contrast CT are limited (NCCT). To evaluate the predictive capability of a deep learning algorithm, "Triage Stroke" (Brainomix 360) in identifying anterior circulation large vessel occlusions (LVO) on NCCT in patients with suspected acute ischemic stroke (AIS). This multi-institutional study analyzed 612 patients with suspected AIS at 3 US comprehensive stroke centers. A balanced cohort of consecutive patients with and without anterior circulation LVO was analyzed. Ground truth was based on concurrent CTA evaluated by site neuroradiologists. The primary outcome was predictive performance for LVO detection. The secondary outcomes were 1) prospective comparison of NCCT LVO detection against general radiologists and subspecialty neuroradiologists, and 2) the influence of NIHSS on the model. Triage Stroke software detected an LVO on NCCT with a 67% sensitivity and 93% specificity. The positive and negative predictive values were 59% and 95%, respectively, with an area under the curve (AUC) of 0.8. The software's sensitivity for LVO detection was significantly higher than the group average of all radiologists (difference = 20.5%; CI, 8.26-32.78; <i>P</i> = .001) and was also higher when separated into general and neuroradiology subgroups. The AUC for NCCT LVO was significantly higher than the group of all readers (difference = 11%; CI, 4%-17%; <i>P</i> < .001), and the nonexpert readers (difference = 13%, CI, 7%-20%; <i>P</i> < .001). The addition of NIHSS to the model yielded a high specificity (99%) and similar sensitivity (65%), resulting in the optimum positive predictive value of all models tested (91%). Triage Stroke software demonstrated strong predictive capabilities for NCCT detection of anterior circulation LVOs outperforming radiologists. Coupled with NIHSS it may simplify identification of endovascular candidates especially in resource-constrained environments worldwide.

To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the boundaries of anatomical compartments. We extracted preliminary segmentations from TotalSegmentator, spine, and body composition models for magnetic resonance tomography (MR) images, then improved them iteratively and retrained an nnUNet model. Using a random retrospective subset of German National Cohort (NAKO), UK Biobank, internal MR and computed tomography (CT) data (Training: 2897 series from 626 subjects, 290 female; mean age 53 ± 16; 3-fold-cross validation (20% hold-out). Internal testing 36 series from 12 subjects, 6 male; mean age 60 ± 11), we segmented 71 structures in torso MR and 72 in CT images: 20 organs, 10 muscles, 19 vessels, 16 bones, ribs in CT, intervertebral discs, spinal cord, spinal canal and body composition (subcutaneous fat, unclassified muscles and visceral fat). For external validation, we used existing automatic organ segmentations, independent ground truth segmentations on gradient echo images, and the Amos data. We used non-parametric bootstrapping for confidence intervals and the Wilcoxon rank-sum test for computing statistical significance. We achieved an average Dice score of 0.90 ± 0.06 on our internal gradient echo test set, which included 71 semantic segmentation labels. Our model ties with the best model on Amos with a Dice of 0,81 ± 0.14, while having a larger field of view and a considerably higher number of structures included. Our work presents a publicly available full-torso segmentation model for MRI and CT images that classifies almost all subject voxels to date. Question No completed MRI segmentation model exists that delineates the true transition boundaries of the anatomical structures of bone and muscles. Findings We provide a simple-to-use model that automatically segments MRI images, that can be utilized as a backbone for computer-aided automatic analysis. Clinical relevance Our segmentation model enables accurate and detailed full-torso segmentation on MRI and CT, improving automated analysis in large-scale epidemiological studies and facilitating more precise body composition and organ assessments for clinical and research applications.

To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) in enhancing the visualization of pancreatic cystic lesions (PCLs) on magnetic resonance cholangiopancreatography (MRCP). This retrospective study included 85 patients who underwent MRCP, comprising 52 patients with PCLs and 33 without. Images reconstructed using SR-DLR were compared with original images. Quantitative metrics included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the common bile duct (CBD) and PCLs, as well as full width at half maximum (FWHM), edge rise distance (ERD), and edge rise slope (ERS) of the CBD and main pancreatic duct (MPD). Qualitative evaluation was conducted by three radiologists, assessing the depiction of PCLs and the MPD, image sharpness, noise, artifacts, overall image quality, and the connection of PCLs and MPD. Quantitative and qualitative metrics were compared using paired t-test and the Wilcoxon signed rank test. SR-DLR significantly enhanced SNR and CNR (p < 0.001). Image sharpness was also enhanced, as shown by lower ERD and higher ERS in both CBD and MPD, together with reduced FWHM of the MPD (p < 0.005). Qualitative assessments indicated improved depiction of PCLs and image sharpness with SR-DLR across all readers (p ≤ 0.017). Most readers also reported improved visualization of the MPD and reduced noise, and overall quality. There was no statistically significant difference in determining the connectivity between PCLs and the MPD. SR-DLR significantly enhances image quality in MRCP, improving visualization of PCLs. These findings suggest that SR-DLR can contribute to appropriate management of PCLs.

While a larger fraction of head and neck squamous cell carcinoma (HNSCC) genomes is characterized by a high prevalence of copy number alterations (CNA-positive), a smaller subset with more favorable oncologic outcome is instead driven by somatic mutations (CNA-negative). We aimed to investigate the radiomic phenotypes of CNA-positive and -negative HNSCCs in contrast CT images. Single nucleotide polymorphism (SNP)-array copy number data were utilized and CNA-based hierarchical clustering of patients was performed to define CNA subclasses. Radiomic features (n=1037) quantifying shape, first-order intensity, and texture were extracted from HNSCC primary tumors in pretherapeutic neck CTs. We performed univariate association analyses and trained, optimized and validated radiomics-based CNA prediction models by combining feature selection algorithms with machine learning classifiers. A total of 522 and 114 patients were included in the copy number and radiomic analyses, respectively. Univariate analysis revealed 190 features from all feature subtypes (shape, first-order, texture) were significantly associated with the CNA status; after multiple testing correction, 29 texture or first-order features remained significant. The best-performing CNA status prediction model utilized a support vector machine classifier, achieving an AUC of 0.71 (95% confidence interval: 0.60-0.83). CNA subgroups exhibit distinct radiomic phenotypes, primarily reflected in texture and intensity characteristics. These findings enhance our understanding of the biological significance of radiomic information in HNSCC. In the clinical setting, as CNA-positive and -negative HNSCCs may emerge as distinct subclasses with unique staging schemes and treatment implications, improved CT radiomics-based prediction models could offer a noninvasive, cost-effective method for CNA subtyping.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the primary cause of dementia, responsible for 60-70% of global cases. It severely affects memory, cognitive function, and daily independence, placing a substantial emotional and economic burden on patients and caregivers. Early and accurate prediction remains difficult due to the high cost of neuroimaging, scarcity of annotated datasets, and the "black-box" nature of most artificial intelligence (AI) models. With the emergence of Healthcare 5.0, the Internet of Medical Things (IoMT) offers new opportunities for patient-centric, real-time monitoring and data-driven diagnosis. This study proposes an IoMT-driven Alzheimer's prediction framework that combines transfer learning (ResNet152) with explainable AI (XAI) to provide both accuracy and interpretability. The publicly available Kaggle Alzheimer's MRI dataset, comprising 33,984 images across four classes (Non-Demented, Very Mild, Mild, and Moderate Demented) was employed. To address class imbalance, a Conditional Wasserstein GAN was applied for synthetic image generation and balanced sampling. The proposed ResNet152-TL-XAI model achieved 97.77% accuracy, with a precision of 0.981, recall of 0.987, F1-score of 0.983, and specificity of 99.13%, outperforming several state-of-the-art methods. Interpretability was ensured through Grad-CAM, SHAP, and LIME, which consistently highlighted clinically relevant brain regions such as the Hippocampus and ventricles, confirming biological plausibility and increasing clinician trust. By integrating IoMT-enabled data acquisition, transfer learning for efficient training, and multi-method XAI for transparency, the proposed pipeline demonstrates strong potential for early, accurate, and interpretable Alzheimer's staging. These results position the framework as a practical candidate for integration into Healthcare 5.0 ecosystems, supporting timely diagnosis, patient monitoring, and personalized interventions.

Brain tumors are a critical medical challenge, requiring accurate and timely diagnosis to improve patient outcomes. Misclassification can significantly reduce life expectancy, emphasizing the need for precise diagnostic methods. Manual analysis of extensive magnetic resonance imaging (MRI) datasets is both labor-intensive and time-consuming, underscoring the importance of an efficient deep learning (DL) model to enhance diagnostic accuracy. This study presents an innovative deep ensemble approach based on transfer learning (TL) for effective brain tumor classification. The proposed methodology incorporates comprehensive preprocessing, data balancing through synthetic data generation (SDG), reconstruction and fine-tuning of TL architectures, and ensemble modeling using Genetic Algorithm-based Weight Optimization (GAWO) and Grid Search-based Weight Optimization (GSWO) used to optimize model weights for enhanced performance. Experiments were performed on the Figshare Contrast-Enhanced MRI (CE-MRI) brain tumor dataset, consisting of 3064 images. The proposed approach demonstrated exceptional performance, achieving classification accuracies of 99.57% with Xception, 99.48% with ResNet50V2, 99.33% with ResNet152V2, 99.39% with InceptionResNetV2, 99.78% with GAWO, and 99.84% with GSWO. The GSWO achieved the highest average accuracy of 99.84% across five-fold cross-validation among other DL models. The comparative analysis highlights the superiority of the proposed model over State of Arts (SOA) works, showcasing its potential to assist neurologists and clinicians in making precise and timely diagnostic decisions. The study concludes that the optimized deep ensemble model is a robust and reliable tool for brain tumor classification.

Host responses during ARDS are highly heterogeneous, contributing to inconsistent therapeutic outcomes. Proteome-based phenotyping may identify biologically and clinically distinct phenotypes to guide precision therapy. In this multicenter cohort study, we used latent class analysis (LCA) of targeted serum proteomics to identify ARDS phenotypes. Serum samples were collected within 72 h of diagnosis to capture early-phase profiles. Validation was conducted in external cohorts. Pathway enrichment assessed molecular heterogeneity. Lung CT scans were analyzed using machine learning-based radiomics to explore phenotypic distinctions. Heterogeneous treatment effects (HTEs) for glucocorticoids and ventilation strategies were evaluated using inverse probability of treatment weighting (IPTW) adjusted Cox regression. A multinomial XGBoost model was developed to classify phenotypes. Among 1048 patients, three inflammatory phenotypes (C1, C2, C3) were identified and validated in two independent cohorts. The phenotype C1 with a larger proportion of poorly/non-inflated lung compartments had the highest 90-day mortality, shock incidence, and fewest ventilator-free days, followed by C3, while C2 patients had the best outcomes (<i>p</i><0.001). Phenotype C1 was characterized by intense innate immune activation, cytokine amplification, and metabolic reprogramming. Phenotype C2 demonstrated immune suppression, enhanced tissue repair, and restoration of anti-inflammatory metabolism. Phenotype C3, comprising the oldest patients, reflected an intermediate state with moderate immune activation and partial immune resolution. Glucocorticoids therapy and higher positive end-expiratory pressure (PEEP) ventilation improved 90-day outcomes in C1 but increased mortality in C2 patients (<i>P</i> _interaction<0.05). Finally, a 12-biomarker classifier can accurately distinguish phenotypes. We identified and validated three proteome-based ARDS phenotypes with distinct clinical, radiographic, and molecular profiles. Their differential treatment responses highlight the potential of biomarker-driven strategies for ARDS precision medicine.

In ultra-high-resolution CT (U-HRCT), longer gantry rotation times are sometimes used to maintain image quality when using a small focal spot. This study aimed to evaluate the impact of gantry rotation time on image quality for deep learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and filtered back projection (FBP). A phantom was scanned on a U-HRCT scanner at four dose levels and four gantry rotation times, with images reconstructed using DLR, MBIR, and FBP algorithms. Image quality was evaluated for noise characteristics and high-contrast resolution. Noise was characterized using the noise power spectrum (NPS) to compute the noise magnitude ratio and central frequency ratio for MBIR and DLR relative to FBP, while high-contrast resolution was determined from the profile curve. MBIR and FBP demonstrated consistent image quality across all rotation times, with no statistically significant differences observed. In contrast, DLR showed significantly lower high-contrast resolution at a 1.0 s rotation time compared to 0.5-0.75 s (p<0.05). At 1.0 s, DLR also exhibited an unfavorable shift of the NPS toward lower frequencies, indicating degraded noise texture. While DLR delivers superior image quality at gantry rotation times of 0.5-0.75s, it exhibits a loss of resolution and altered noise texture at 1.0 s. This degradation is likely attributable to the algorithm's limitations when processing data distributions that were underrepresented in its training set. Therefore, to optimize diagnostic performance, scan parameters must be carefully tailored to the specific reconstruction algorithm.