Background and AimsEchocardiography serves as a cornerstone of cardiovascular diagnostics through multiple standardized imaging views. While recent AI foundation models demonstrate superior capabilities across cardiac imaging tasks, their massive computational requirements and reliance on large-scale datasets create accessibility barriers, limiting AI development to well-resourced institutions. Vector embedding approaches offer promising solutions by leveraging compact representations from original medical images for downstream applications. Furthermore, demographic fairness remains critical, as AI models may incorporate biases that confound clinically relevant features. We developed a multi-view encoder framework to address computational accessibility while investigating demographic fairness challenges. MethodsWe utilized the MIMIC-IV-ECHO dataset (7,169 echocardiographic studies) to develop a transformer-based multi-view encoder that aggregates view-level representations into study-level embeddings. The framework incorporated adversarial learning to suppress demographic information while maintaining clinical performance. We evaluated performance across 21 binary classification tasks encompassing echocardiographic measurements and clinical diagnoses, comparing against foundation model baselines with varying adversarial weights. ResultsThe multi-view encoder achieved a mean improvement of 9.0 AUC points (12.0% relative improvement) across clinical tasks compared to foundation model embeddings. Performance remained robust with limited echocardiographic views compared to the conventional approach. However, adversarial learning showed limited effectiveness in reducing demographic shortcuts, with stronger weighting substantially compromising diagnostic performance. ConclusionsOur framework democratizes advanced cardiac AI capabilities, enabling substantial diagnostic improvements without massive computational infrastructure. While algorithmic approaches to demographic fairness showed limitations, the multi-view encoder provides a practical pathway for broader AI adoption in cardiovascular medicine with enhanced efficiency in real-world clinical settings. Structured graphical abstract or graphical abstractO_ST_ABSKey QuestionC_ST_ABSCan multi-view encoder frameworks achieve superior diagnostic performance compared to foundation model embeddings while reducing computational requirements and maintaining robust performance with fewer echocardiographic views for cardiac AI applications? Key FindingMulti-view encoder achieved 12.0% relative improvement (9.0 AUC points) across 21 cardiac tasks compared to foundation model baselines, with efficient 512-dimensional vector embeddings and robust performance using fewer echocardiographic views. Take-home MessageVector embedding approaches with attention-based multi-view integration significantly improve cardiac diagnostic performance while reducing computational requirements, offering a pathway toward more efficient AI implementation in clinical settings. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/25333725v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): [email protected]@a75818org.highwire.dtl.DTLVardef@88a588org.highwire.dtl.DTLVardef@12bad06_HPS_FORMAT_FIGEXP M_FIG C_FIG Translational PerspectiveOur proposed multi-view encoder framework overcomes critical barriers to the widespread adoption of artificial intelligence in echocardiography. By dramatically reducing computational requirements, the multi-view encoder approach allows smaller healthcare institutions to develop sophisticated AI models locally. The framework maintains robust performance with fewer echocardiographic examinations, which addresses real-world clinical constraints where comprehensive imaging is not feasible due to patient factors or time limitations. This technology provides a practical way to democratize advanced cardiac AI capabilities, which could improve access to cardiovascular care across diverse healthcare settings while reducing dependence on proprietary datasets and massive computational resources.

The lumen and external elastic lamina contour delineation is crucial for quantitative analyses of intravascular ultrasound (IVUS) images. However, the various artifacts in IVUS images pose substantial challenges for accurate delineation. Existing mask-based methods often produce anatomically implausible contours in artifact-affected images, while contour-based methods suffer from the over-smooth problem within the artifact regions. In this paper, we directly regress the contour pairs instead of mask-based segmentation. A coupled contour representation is adopted to learn a low-dimensional contour signature space, where the embedded anatomical prior enables the model to avoid producing unreasonable results. Further, a PIoU loss is proposed to capture the overall shape of the contour points and maximize the similarity between the regressed contours and manually delineated contours with various irregular shapes, alleviating the over-smooth problem. For the images with severe artifacts, a difficulty-aware training strategy is designed for contour regression, which gradually guides the model focus on hard samples and improves contour localization accuracy. We evaluate the proposed framework on a large IVUS dataset, consisting of 7204 frames from 185 pullbacks. The mean Dice similarity coefficients of the method for the lumen and external elastic lamina are 0.951 and 0.967, which significantly outperforms other state-of-the-art (SOTA) models. All regressed contours in the test images are anatomically plausible. On the public IVUS-2011 dataset, the proposed method attains comparable performance to the SOTA models with the highest processing speed at 100 fps. The code is available at https://github.com/SMU-MedicalVision/ContourRegression.

Detecting clinically significant prostate cancer (csPCa) remains a top priority in delivering high-quality care, yet consensus on an optimal diagnostic pathway is constantly evolving. In this study, we present an innovative diagnostic approach, leveraging a machine learning model tailored to the emerging role of prostate micro-ultrasound (micro-US) in the setting of csPCa diagnosis. We queried our prospective database for patients who underwent Micro-US for a clinical suspicious of prostate cancer. CsPCa was defined as any Gleason group grade > 1. Primary outcome was the development of a diagnostic pathway which implements clinical and radiological findings using machine learning algorithm. The dataset was divided into training (70%) and testing subsets. Boruta algorithms was used for variable selection, then based on the importance coefficients multivariable logistic regression model (MLR) was fitted to predict csPCA. Classification and Regression Tree (CART) model was fitted to create the decision tree. Accuracy of the model was tested using receiver characteristic curve (ROC) analysis using estimated area under the curve (AUC). Overall, 1422 patients were analysed. Multivariable LR revealed PRI-MUS score ≥ 3 (OR 4.37, p < 0.001), PI-RADS score ≥ 3 (OR 2.01, p < 0.001), PSA density ≥ 0.15 (OR 2.44, p < 0.001), DRE (OR 1.93, p < 0.001), anterior lesions (OR 1.49, p = 0.004), prostate cancer familiarity (OR 1.54, p = 0.005) and increasing age (OR 1.031, p < 0.001) as the best predictors for csPCa, demonstrating an AUC in the validation cohort of 83%, 78% sensitivity, 72.1% specificity and 81% negative predictive value. CART analysis revealed elevated PRIMUS score as the main node to stratify our cohort. By integrating clinical features, serum biomarkers, and imaging findings, we have developed a point of care model that accurately predicts the presence of csPCa. Our findings support a paradigm shift towards adopting MicroUS as a first level diagnostic tool for csPCa detection, potentially optimizing clinical decision making. This approach could improve the identification of patients at higher risk for csPca and guide the selection of the most appropriate diagnostic exams. External validation is essential to confirm these results.

The shape and size of the placenta are closely related to fetal development in the second and third trimesters of pregnancy. Accurately segmenting the placental contour in ultrasound images is a challenge because it is limited by image noise, fuzzy boundaries, and tight clinical resources. To address these issues, we propose MCBL-UNet, a novel lightweight segmentation framework that combines the long-range modeling capabilities of Mamba and the local feature extraction advantages of convolutional neural networks (CNNs) to achieve efficient segmentation through multi-information fusion. Based on a compact 6-layer U-Net architecture, MCBL-UNet introduces several key modules: a boundary enhancement module (BEM) to extract fine-grained edge and texture features; a multi-dimensional global context module (MGCM) to capture global semantics and edge information in the deep stages of the encoder and decoder; and a parallel channel spatial attention module (PCSAM) to suppress redundant information in skip connections while enhancing spatial and channel correlations. To further improve feature reconstruction and edge preservation capabilities, we introduce an attention downsampling module (ADM) and a content-aware upsampling module (CUM). MCBL-UNet has achieved excellent segmentation performance on multiple medical ultrasound datasets (placenta, gestational sac, thyroid nodules). Using only 1.31M parameters and 1.26G FLOPs, the model outperforms 13 existing mainstream methods in key indicators such as Dice coefficient and mIoU, showing a perfect balance between high accuracy and low computational cost. This model is not only suitable for resource-constrained clinical environments, but also provides a new idea for introducing the Mamba structure into medical image segmentation.

BackgroundIron overload promotes atherosclerosis in mice and causes vascular dysfunction in humans with Hemochromatosis. However, data are controversial on whether systemic iron availability within physiological limits affects the pathogenesis of atherosclerosis. We, therefore, performed an individual participant data (IPD) meta-analysis and studied the association between serum iron biomarkers with common carotid intima-media thickness (CC-IMT); in addition, since sex influences iron metabolism and vascular aging, we studied if there are sex-specific differences. MethodsWe pooled the IPD and analysed the data on adults (age[&ge;]18y) by orthogonal approaches: machine learning (ML) and a single-stage meta-analysis. For ML, we tuned a gradient-boosted tree regression model (XGBoost) and subsequently, we interpreted the features using variable importance. For the single-stage metaanalysis, we examined the association between iron biomarkers and CC-IMT using spline-based linear mixed models, accounting for sex interactions and study-specific effects. To confirm robustness, we repeated analyses on imputed data using multivariable regression adjusted for key covariates identified through machine learning. Further, subgroup analyses were performed in children and adolescents (age<18y). In addition, to evaluate causality, we used UK Biobank data to examine associations between the hemochromatosis (HFE) genotypes (C282Y/H63D) and mean CC-IMT in [~]42,500 participants with carotid ultrasound data, using sex-stratified linear regression (adjusted for age, assessment centre, and genetic principal components). ResultsWe included IPD from 21 studies (N=10,807). The application of the ML model showed moderate predictive performance and identified iron biomarkers (transferrin, ferritin, transferrin saturation, and iron) as key features for IMT prediction. Multivariable analyses showed non-linear sex-specific relationships for ferritin and transferrin with CC-IMT: ferritin showed a significant positive association, and transferrin showed negative associations at specific ranges, both only among females. No significant associations were found between CC-IMT in those with HFE genotypes in either sex in the UK Biobank. ConclusionOur observational data show that iron biomarkers - ferritin and transferrin are non-linearly associated with CC-IMT specifically in females, while a significant causal association between the HFE genotype and CC-IMT could not be demonstrated in the UK Biobank data. We conclude that the observational associations may not only be explained by causal effects of iron on the arterial wall thickness, but also in part be driven by residual confounding factors such as inflammation. Other: No financial support was received for this meta-analysis. The protocol for this study is registered in the PROSPERO database (CRD42020155429; https://www.crd.york.ac.uk/).

Ultrasound computed tomography (USCT) is a radiation-free, high-resolution modality but remains limited for musculoskeletal imaging due to conventional ray-based reconstructions that neglect strong scattering. We propose a generative neural physics framework that couples generative networks with physics-informed neural simulation for fast, high-fidelity 3D USCT. By learning a compact surrogate of ultrasonic wave propagation from only dozens of cross-modality images, our method merges the accuracy of wave modeling with the efficiency and stability of deep learning. This enables accurate quantitative imaging of in vivo musculoskeletal tissues, producing spatial maps of acoustic properties beyond reflection-mode images. On synthetic and in vivo data (breast, arm, leg), we reconstruct 3D maps of tissue parameters in under ten minutes, with sensitivity to biomechanical properties in muscle and bone and resolution comparable to MRI. By overcoming computational bottlenecks in strongly scattering regimes, this approach advances USCT toward routine clinical assessment of musculoskeletal disease.

Accurate diagnosis of thyroid nodules using ultrasonography is a highly valuable, but challenging task. With the emergence of artificial intelligence, deep learning based methods can provide assistance to radiologists, whose performance heavily depends on the quantity and quality of training data, but current ultrasound image datasets for thyroid nodule either directly utilize the TI-RADS assessments as labels or are not publicly available. Faced with these issues, an open-access ultrasound image dataset for thyroid nodule detection and classification is proposed, i.e. the TN5000, which comprises 5,000 B-mode ultrasound images of thyroid nodule, as well as complete annotations and biopsy confirmations by expert radiologists. Additionally, the statistical characteristics of this proposed dataset have been analyzed clearly, some baseline methods for the detection and classification of thyroid nodules are recommended as the benchmark, along with their evaluation results. To our best knowledge, TN5000 is the largest open-access ultrasound image dataset of thyroid nodule with professional labeling, and is the first ultrasound image dataset designed both for the thyroid nodule detection and classification. These kinds of images with annotations can contribute to analyze the intrinsic properties of thyroid nodules and to determine the necessity of FNA biopsy, which are crucial in ultrasound diagnosis.

Abdominal haemorrhage is a life-threatening condition requiring prompt detection to enable timely intervention. Conventional ultrasound (US) is widely used but is highly operator-dependent, limiting its reliability outside clinical settings. In anatomical regions, in particular Morison's Pouch, US provides a higher detection reliability due to the preferential accumulation of free fluid in dependent areas. Recent advancements in artificial intelligence (AI)-integrated point-of-care US (POCUS) systems show promise for use in emergency, pre-hospital, military, and resource-limited environments. This systematic review evaluates the performance of AI-driven POCUS systems for detecting and estimating abdominal haemorrhage. A systematic search of Scopus, PubMed, EMBASE, and Web of Science (2014-2024) identified seven studies with sample sizes ranging from 94 to 6608 images and patient numbers ranging between 78 and 864 trauma patients. AI models, including YOLOv3, U-Net, and ResNet50, demonstrated high diagnostic accuracy, with sensitivity ranging from 88% to 98% and specificity from 68% to 99%. Most studies utilized 2D US imaging and conducted internal validation, typically employing systems such as the Philips Lumify and Mindray TE7. Model performance was predominantly assessed using internal datasets, wherein training and evaluation were performed on the same dataset. Of particular note, only one study validated its model on an independent dataset obtained from a different clinical setting. This limited use of external validation restricts the ability to evaluate the applicability of AI models across diverse populations and varying imaging conditions. Moreover, the Focused Assessment with Sonography in Trauma (FAST) is a protocol drive US method for detecting free fluid in the abdominal cavity, primarily in trauma cases. However, while it is commonly used to assess the right upper quadrant, particularly Morison's pouch, which is gravity-dependent and sensitive for early haemorrhage its application to other abdominal regions, such as the left upper quadrant and pelvis, remains underexplored. This is clinically significant, as fluid may preferentially accumulate in these areas depending on the mechanism of injury, patient positioning, or time since trauma, underscoring the need for broader anatomical coverage in AI applications. Researchers aiming to address the current reliance on 2D imaging and the limited use of external validation should focus future studies on integrating 3D imaging and utilising diverse, multicentre datasets to improve the reliability and generalizability of AI-driven POCUS systems for haemorrhage detection in trauma care.

Ultrasound imaging is critical for clinical diagnostics, providing insights into various diseases and organs. However, artificial intelligence (AI) in this field faces challenges, such as the need for large labeled datasets and limited task-specific model applicability, particularly due to ultrasound's low signal-to-noise ratio (SNR). To overcome these, we introduce the Ultrasound Representation Foundation Model (URFM), designed to learn robust, generalizable representations from unlabeled ultrasound images, enabling label-efficient adaptation to diverse diagnostic tasks. URFM is pre-trained on over 1M images from 15 major anatomical organs using representation-based masked image modeling (MIM), an advanced self-supervised learning. Unlike traditional pixel-based MIM, URFM integrates high-level representations from BiomedCLIP, a specialized medical vision-language model, to address the low SNR issue. Extensive evaluation shows that URFM outperforms state-of-the-art methods, offering enhanced generalization, label efficiency, and training-time efficiency. URFM's scalability and flexibility signal a significant advancement in diagnostic accuracy and clinical workflow optimization in ultrasound imaging.

Accurate, noninvasive diagnosis of compensated advanced chronic liver disease (cACLD) is essential for effective clinical management but remains challenging. This study aimed to develop a deep learning-based radiomics model using international multicenter data and to evaluate its performance by comparing it to the two-dimensional shear wave elastography (2D-SWE) cut-off method covering multiple countries or regions, etiologies, and ultrasound device manufacturers. This retrospective study included 1937 adult patients with chronic liver disease due to hepatitis B, hepatitis C, or metabolic dysfunction-associated steatotic liver disease. All patients underwent 2D-SWE imaging and liver biopsy at 17 centers across China, Japan, and Europe using devices from three manufacturers (SuperSonic Imagine, General Electric, and Mindray). The proposed generalized deep learning radiomics of elastography model integrated both elastographic images and liver stiffness measurements and was trained and tested on stratified internal and external datasets. A total of 1937 patients with 9472 2D-SWE images were included in the statistical analysis. Compared to 2D-SWE, the model achieved a higher area under the receiver operating characteristic curve (AUC) (0.89 vs 0.83, P = 0.025). It also achieved a highly consistent diagnosis across all subanalyses (P values: 0.21-0.91), whereas 2D-SWE exhibited different AUCs in the country or region (P < 0.001) and etiology (P = 0.005) subanalyses but not in the manufacturer subanalysis (P = 0.24). The model demonstrated more accurate and robust performance in noninvasive cACLD diagnosis than 2D-SWE across different countries or regions, etiologies, and manufacturers.