Simulated ultrasound image sequences are key for training and validating machine learning algorithms for left ventricular strain estimation. Several simulation pipelines have been proposed to generate sequences with corresponding ground truth motion, but they suffer from limited realism as they do not consider speckle decorrelation. In this work, we address this limitation by proposing an improved simulation framework that explicitly accounts for speckle decorrelation. Our method builds on an existing ultrasound simulation pipeline by incorporating a dynamic model of speckle variation. Starting from real ultrasound sequences and myocardial segmentations, we generate meshes that guide image formation. Instead of applying a fixed ratio of myocardial and background scatterers, we introduce a coherence map that adapts locally over time. This map is derived from correlation values measured directly from the real ultrasound data, ensuring that simulated sequences capture the characteristic temporal changes observed in practice. We evaluated the realism of our approach using ultrasound data from 98 patients in the CAMUS database. Performance was assessed by comparing correlation curves from real and simulated images. The proposed method achieved lower mean absolute error compared to the baseline pipeline, indicating that it more faithfully reproduces the decorrelation behavior seen in clinical data.

Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Ultrasound imaging, widely used due to its safety and cost-effectiveness, plays a key role in early detection, especially in patients with dense breast tissue. This paper presents a comprehensive study on the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images. Using datasets such as BUSI, BUS-BRA, and BrEaST-Lesions USG, we evaluate classical machine learning models (SVM, KNN) and deep convolutional neural networks (ResNet-18, EfficientNet-B0, GoogLeNet). Experimental results show that ResNet-18 achieves the highest accuracy (99.7%) and perfect sensitivity for malignant lesions. Classical ML models, though outperformed by CNNs, achieve competitive performance when enhanced with deep feature extraction. Grad-CAM visualizations further improve model transparency by highlighting diagnostically relevant image regions. These findings support the integration of AI-based diagnostic tools into clinical workflows and demonstrate the feasibility of deploying high-performing, interpretable systems for ultrasound-based breast cancer detection.

Carotid artery plaques, a hallmark of atherosclerosis, are key risk indicators for ischemic stroke, a major global health burden with 101 million cases and 6.65 million deaths in 2019. Early ultrasound detection is vital but hindered by manual analysis limitations. Machine learning (ML) offers a promising solution for automated plaque detection, yet its comparative performance is underexplored. This systematic review and meta-analysis evaluates ML models for carotid plaque detection using ultrasound. We searched PubMed, Scopus, Embase, Web of Science, and ProQuest for studies on ML-based carotid plaque detection with ultrasound, following PRISMA guidelines. Eligible studies reported diagnostic metrics and used a reference standard. Data on study characteristics, ML models, and performance were extracted, with risk of bias assessed via PROBAST+AI. Pooled sensitivity, specificity, AUROC were calculated using STATA 18 with MIDAS and METADTA modules. Of ten studies, eight were meta-analyzed (200-19,751 patients) Best models showed a pooled sensitivity 0.94 (95% CI: 0.88-0.97), specificity 0.95 (95% CI: 0.86-0.98), AUROC 0.98 (95% CI: 0.97-0.99), and DOR 302 (95% CI: 54-1684), with high heterogeneity (I² = 90%) and no publication bias. ML models show promise in carotid plaque detection, supporting potential clinical integration for stroke prevention, though high heterogeneity and potential bias highlight the need for standardized validation.

<b><i>Background:</i></b> Oncologic emergencies in critically ill cancer patients frequently require rapid, real-time assessment of tumor responses to therapeutic interventions. However, conventional imaging modalities such as computed tomography and magnetic resonance imaging are often impractical in intensive care units (ICUs) due to logistical constraints and patient instability. Super-resolution ultrasound (SR-US) imaging has emerged as a promising noninvasive alternative, facilitating bedside evaluation of tumor microvascular dynamics with exceptional spatial resolution. This study assessed the clinical utility of real-time SR-US imaging in monitoring tumor perfusion changes during emergency management in oncological ICU settings. <b><i>Methods:</i></b> In this prospective observational study, critically ill patients with oncologic emergencies underwent bedside SR-US imaging before and after the initiation of emergency therapy (e.g., corticosteroids, decompression, or chemotherapy). SR-US was employed to quantify microvascular parameters, including perfusion density and flow heterogeneity. Data processing incorporated artificial intelligence for real-time vessel segmentation and quantitative analysis. <b><i>Results:</i></b> SR-US imaging successfully detected perfusion changes within hours of therapy initiation. A significant correlation was observed between reduced tumor perfusion and clinical improvement, including symptom relief and shorter ICU stay. This technology enables visualization of microvessels as small as 30 µm, surpassing conventional ultrasound limits. No adverse events were reported with the use of contrast microbubbles. In addition, SR-US imaging reduces the need for transportation to radiology departments, thereby optimizing ICU workflow. <b><i>Conclusions:</i></b> Real-time SR-US imaging offers a novel, bedside-compatible method for evaluating tumor vascular response during the acute phase of oncological emergencies. Its integration into ICU care pathways could enhance timely decision-making, reduce reliance on static imaging, and support personalized cancer management. Further multicenter validation is required.

Technological innovations in robot-assisted ultrasound (RAUS) have remarkably advanced the development of precision and intelligent medical imaging diagnosis. This study aims to use bibliometric methods to systematically analyze the technological evolution and research frontiers in the RAUS field, providing valuable insights for future research. This study used the Web of Science Core Collection database to retrieve English-language research papers and reviews related to RAUS published between 2000 and 2024. Using analytical tools such as R (with the Bibliometrix package), VOSviewer, and CiteSpace, the study conducted a bibliometric analysis from multiple angles, including literature distribution, collaboration networks, and knowledge clustering. The visualization of analysis results comprehensively revealed the hot topics and emerging research frontiers within the RAUS field. The results reveal an exponential growth trend in RAUS research, with China leading in publication output (accounting for 28.51% of total publications), while the USA leads in terms of citation impact and international collaboration networks. Institutions such as Johns Hopkins University and Chinese Academy of Sciences emerge as highly productive core contributors. The research field has formed a multidimensional interdisciplinary landscape encompassing "mathematical sciences-engineering technology-medical health." The focus is on the integration of artificial intelligence (AI) and its clinical application translation. From 2000 to 2014, the development of "mobile robots" laid the cornerstone for further advancements. From 2015 to 2018, research focused on the development of "surgery" and "tumors" for medical applications. From 2019 to 2024, the core focus will be on "medical robots and systems," "artificial intelligence" and "robotic ultrasound," highlighting the transformation of technology into an AI-driven model. This study systematically reviewed the development of RAUS through bibliometric methods, enriching academic understanding of the field and providing valuable guidance for future technological iterations, clinical translation, and global cooperation to ultimately achieve precision medicine and balanced medical resources.

Diagnostic ultrasound has long filled a crucial niche in medical imaging thanks to its portability, affordability, and favorable safety profile. Now, multi-view hardware and deep-learning-based image reconstruction algorithms promise to extend this niche to increasingly sophisticated applications, such as volume rendering and long-term organ monitoring. However, progress on these fronts is impeded by the complexities of ultrasound electronics and by the scarcity of high-fidelity radiofrequency data. Evidently, there is a critical need for tools that enable rapid ultrasound prototyping and generation of synthetic data. We meet this need with MUSiK, the first open-source ultrasound simulation library expressly designed for multi-view acoustic simulations of realistic anatomy. This library covers the full gamut of image acquisition: building anatomical digital phantoms, defining and positioning diverse transducer types, running simulations, and reconstructing images. In this paper, we demonstrate several use cases for MUSiK. We simulate in vitro multi-view experiments and compare the resolution and contrast of the resulting images. We then perform multiple conventional and experimental in vivo imaging tasks, such as 2D scans of the kidney, 2D and 3D echocardiography, 2.5D tomography of large regions, and 3D tomography for lesion detection in soft tissue. Finally, we introduce MUSiK's Bayesian reconstruction framework for multi-view ultrasound and validate an original SNR-enhancing reconstruction algorithm. We anticipate that these unique features will seed new hypotheses and accelerate the overall pace of ultrasound technological development. The MUSiK library is publicly available at github.com/norway99/MUSiK.

A predictive model of cervical lymph node metastasis and metastasis volume was constructed based on a machine learning algorithm and ultrasound characteristics before surgery. A retrospective analysis was conducted on 573 cases of PTC patients who underwent surgery in our institution, from 2017 to 2022. Patient demographic and clinical characteristics were systematically collected. Feature selection was performed using univariate analysis, Logistic regression (LR) analysis. Statistically significant variables were identified using a threshold of p < 0.05. Predictive models for cervical lymph node metastasis and metastatic volume in papillary thyroid carcinoma were constructed using advanced machine learning algorithms: K-Nearest Neighbors (KNN), Gradient Boosting Machine (XGBoost), and Support Vector Machine (SVM). Model performance was rigorously assessed using validation cohort data, evaluating area under the Receiver Operating Characteristic (ROC) curve, sensitivity, specificity, and accuracy. In this retrospective study of 573 patients (320 had lymph node metastasis, 127 had small volume lymph node metastasis, and 193 had medium-volume lymph node metastasis). In the model predicting the neck lymph node metastasis, the Gradient Boosting method exhibited the best performance, with an area under the ROC curve of 0.784, sensitivity of 76.2%, specificity of 70.6%, and accuracy of 73.8%. In the model predicting the metastatic volume in neck lymph nodes for PTC, the Gradient Boosting method also demonstrated the best performance, with an area under the ROC curve of 0.779, sensitivity of 71.7%, specificity of 75.9%, and accuracy of 74.4%. Machine learning-based predictive models integrating preoperative ultrasound features demonstrate robust performance in stratifying neck lymph node metastasis risk for PTC patients. These models optimize surgical planning by guiding lymph node dissection extent and individualizing treatment strategies, potentially reducing unnecessary extensive surgeries. The integration of advanced computational techniques with clinical imaging provides a data-driven paradigm for preoperative risk assessment in thyroid oncology.

Threatened miscarriage (TM), defined as first-trimester vaginal bleeding with a closed cervix and detectable fetal cardiac activity, affects up to 30 % of clinically recognised pregnancies and is linked to increased risk of adverse outcomes. This study evaluates the predictive value of first-trimester ultrasound (US) and biochemical (BC) markers in determining outcomes among women with TM symptoms. This prospective cohort study recruited 118 women with viable singleton pregnancies (5⁺⁰ to 12⁺⁶ weeks' gestation) from Malta's national public hospital between January 2023 and June 2024. Participants underwent US and BC assessment, along with collection of clinical and sociodemographic data. Pregnancy outcomes were followed to term and classified as live birth or loss. Univariate logistic regression identified individual predictors. Multivariate logistic regression (MLR) and random forest (RF) modelling assessed combined predictive performance. Among 118 TM cases, 77 % resulted in live birth, 23 % in loss. MLR identified progesterone, cervical length, mean gestational sac diameter (MGSD), trophoblast thickness, sFlt-1:PlGF ratio, and maternal age as significant predictors. Higher progesterone, cervical length, MGSD, and sFlt-1:PlGF ratio reduced risk, while maternal age over 35 increased it. MLR achieved 82.7 % accuracy (AUC = 0.89). RF improved accuracy to 93.1 % (AUC = 0.97), confirming the combined predictive value of US and BC markers. US and BC markers hold predictive value in TM. Machine learning, particularly RF, may improve early clinical risk stratification. This tool may support timely decision-making and personalised monitoring, intervention, and counselling for women with TM.

Musculoskeletal ultrasound plays an important role in facilitating diagnostic and therapeutic decisions in rheumatic diseases. This article discusses the utility of ultrasound in rheumatoid arthritis, spondyloarthropathy and crystal arthropathy. This article also highlights the implementation challenges and the emerging role of artificial intelligence in enhancing musculoskeletal ultrasound. .

BackgroundCarotid plaque presence is associated with cardiovascular risk, even among asymptomatic individuals. While deep learning has shown promise for carotid plaque phenotyping in patients with advanced atherosclerosis, its application in population-based settings of asymptomatic individuals remains unexplored. MethodsWe developed a YOLOv8-based model for plaque detection using carotid ultrasound images from 19,499 participants of the population-based UK Biobank (UKB) and fine-tuned it for external validation in the BiDirect study (N = 2,105). Cox regression was used to estimate the impact of plaque presence and count on major cardiovascular events. To explore the genetic architecture of carotid atherosclerosis, we conducted a genome-wide association study (GWAS) meta-analysis of the UKB and CHARGE cohorts. Mendelian randomization (MR) assessed the effect of genetic predisposition to vascular risk factors on carotid atherosclerosis. ResultsOur model demonstrated high performance with accuracy, sensitivity, and specificity exceeding 85%, enabling identification of carotid plaques in 45% of the UKB population (aged 47-83 years). In the external BiDirect cohort, a fine-tuned model achieved 86% accuracy, 78% sensitivity, and 90% specificity. Plaque presence and count were associated with risk of major adverse cardiovascular events (MACE) over a follow-up of up to seven years, improving risk reclassification beyond the Pooled Cohort Equations. A GWAS meta-analysis of carotid plaques uncovered two novel genomic loci, with downstream analyses implicating targets of investigational drugs in advanced clinical development. Observational and MR analyses showed associations between smoking, LDL cholesterol, hypertension, and odds of carotid atherosclerosis. ConclusionsOur model offers a scalable solution for early carotid plaque detection, potentially enabling automated screening in asymptomatic individuals and improving plaque phenotyping in population-based cohorts. This approach could advance large-scale atherosclerosis research. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/24315675v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): [email protected]@27a04corg.highwire.dtl.DTLVardef@18cef18org.highwire.dtl.DTLVardef@1a53d8f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACT.C_FLOATNO ASCVD - Atherosclerotic Cardiovascular Disease, CVD - Cardiovascular disease, PCE - Pooled Cohort Equations, TP- true positive, FN - False Negative, FP - False Positive, TN - True Negative, GWAS - Genome-Wide Association Study. C_FIG CLINICAL PERSPECTIVECarotid ultrasound is a well-established method for assessing subclinical atherosclerosis with potential to improve cardiovascular risk assessment in asymptomatic individuals. Deep learning could automate plaque screening and enable processing of large imaging datasets, reducing the need for manual annotation. Integrating such large-scale carotid ultrasound datasets with clinical, genetic, and other relevant data can advance cardiovascular research. Prior studies applying deep learning to carotid ultrasound have focused on technical tasks-plaque classification, segmentation, and characterization-in small sample sizes of patients with advanced atherosclerosis. However, they did not assess the potential of deep learning in detecting plaques in asymptomatic individuals at the population level. We developed an efficient deep learning model for the automated detection and quantification of early carotid plaques in ultrasound imaging, primarily in asymptomatic individuals. The model demonstrated high accuracy and external validity across population-based cohort studies. Predicted plaque prevalence aligned with known cardiovascular risk factors. Importantly, predicted plaque presence and count were associated with future cardiovascular events and improved reclassification of asymptomatic individuals into clinically meaningful risk categories. Integrating our model predictions with genetic data identified two novel loci associated with carotid plaque presence--both previously linked to cardiovascular disease--highlighting the models potential for population-scale atherosclerosis research. Our model provides a scalable solution for automated carotid plaque phenotyping in ultrasound images at the population level. These findings support its use for automated screening in asymptomatic individuals and for streamlining plaque phenotyping in large cohorts, thereby advancing research on subclinical atherosclerosis in the general population.