Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tumors, from benign types like leiomyomas. Misdiagnosis can lead to unnecessary interventions or delayed treatment. To address this challenge, we developed ECMAI-WME, a parallel fusion deep learning model integrating white light endoscopy (WLE) and microprobe endoscopic ultrasonography (EUS), to improve SEL classification and lesion characterization. A total of 523 SELs from four hospitals were used to develop serial and parallel fusion AI models. The Parallel Model, demonstrating superior performance, was designated as ECMAI-WME. The model was tested on an external validation cohort (n = 88) and a multicenter test cohort (n = 274). Diagnostic performance, lesion characterization, and clinical decision-making support were comprehensively evaluated and compared with endoscopists' performance. The ECMAI-WME model significantly outperformed endoscopists in diagnostic accuracy (96.35% vs. 63.87-86.13%, p < 0.001) and treatment decision-making accuracy (96.35% vs. 78.47-86.13%, p < 0.001). It achieved 98.72% accuracy in internal validation, 94.32% in external validation, and 96.35% in multicenter testing. For distinguishing gastric GISTs from leiomyomas, the model reached 91.49% sensitivity, 100% specificity, and 96.38% accuracy. Lesion characteristics were identified with a mean accuracy of 94.81% (range: 90.51-99.27%). The model maintained robust performance despite class imbalance, confirmed by five complementary analyses. Subgroup analyses showed consistent accuracy across lesion size, location, or type (p > 0.05), demonstrating strong generalizability. The ECMAI-WME model demonstrates excellent diagnostic performance and robustness in the multiclass SEL classification and characterization, supporting its potential for real-time deployment to enhance diagnostic consistency and guide clinical decision-making.

To construct a multimodal ultrasound (US) radiomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer and evaluated its application value in predicting ALNM and patient prognosis. From March 2014 to December 2022, data from 682 breast cancer patients from four hospitals were collected, including preoperative grayscale US, color Doppler flow imaging (CDFI), contrast-enhanced ultrasound (CEUS) imaging data, and clinical information. Data from the First Medical Center of PLA General Hospital were used as the training and internal validation sets, while data from Peking University First Hospital, the Cancer Hospital of the Chinese Academy of Medical Sciences, and the Fourth Medical Center of PLA General Hospital were used as the external validation set. LASSO regression was employed to select radiomic features (RFs), while eight machine learning algorithms were utilized to construct radiomic models based on US, CDFI, and CEUS. The prediction efficiency of ALNM was assessed to identify the optimal model. In the meantime, Radscore was computed and integrated with immunoinflammatory markers to forecast Disease-Free Survival (DFS) in breast cancer patients. Follow-up methods included telephone outreach and in-person hospital visits. The analysis employed Cox regression to pinpoint prognostic factors, while clinical-imaging models were developed accordingly. The performance of the model was evaluated using the C-index, Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). In the training cohort (n = 400), 40% of patients had ALNM, with a mean age of 55 ± 10 years. The US + CDFI + CEUS-based radiomics model achieved Area Under the Curves (AUCs) of 0.88, 0.81, and 0.77 for predicting N0 versus N+ (≥ 1) in the training, internal, and external validation sets, respectively, outperforming the US-only model (P < 0.05). For distinguishing N+ (1-2) from N+ (≥ 3), the model achieved AUCs of 0.89, 0.74, and 0.75. Combining radiomics scores with clinical immunoinflammatory markers (platelet count and neutrophil-to-lymphocyte ratio) yielded a clinical-radiomics model predicting disease-free survival (DFS), with C-indices of 0.80, 0.73, and 0.79 across the three cohorts. In the external validation cohort, the clinical-radiomics model achieved higher AUCs for predicting 2-, 3-, and 5-year DFS compared to the clinical model alone (2-year: 0.79 vs. 0.66; 3-year: 0.83 vs. 0.70; 5-year: 0.78 vs. 0.64; all P < 0.05). Calibration and decision curve analyses demonstrated good model agreement and clinical utility. The multimodal ultrasound radiomics model based on US, CDFI, and CEUS could effectively predict ALNM in breast cancer. Furthermore, the combined application of radiomics and immune inflammation markers might predict the DFS of breast cancer patients to some extent.

Thyroid cancer is one of the most common endocrine malignancies. Its incidence has steadily increased in recent years. Distinguishing between benign and malignant thyroid nodules (TNs) is challenging due to their overlapping imaging features. The rapid advancement of artificial intelligence (AI) in medical image analysis, particularly deep learning (DL) algorithms, has provided novel solutions for automated TN detection. However, existing studies exhibit substantial heterogeneity in diagnostic performance. Furthermore, no systematic evidence-based research comprehensively assesses the diagnostic performance of DL models in this field. This study aimed to execute a systematic review and meta-analysis to appraise the performance of DL algorithms in diagnosing TN malignancy, identify key factors influencing their diagnostic efficacy, and compare their accuracy with that of clinicians in image-based diagnosis. We systematically searched multiple databases, including PubMed, Cochrane, Embase, Web of Science, and IEEE, and identified 41 eligible studies for systematic review and meta-analysis. Based on the task type, studies were categorized into segmentation (n=14) and detection (n=27) tasks. The pooled sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were calculated for each group. Subgroup analyses were performed to examine the impact of transfer learning and compare model performance against clinicians. For segmentation tasks, the pooled sensitivity, specificity, and AUC were 82% (95% CI 79%-84%), 95% (95% CI 92%-96%), and 0.91 (95% CI 0.89-0.94), respectively. For detection tasks, the pooled sensitivity, specificity, and AUC were 91% (95% CI 89%-93%), 89% (95% CI 86%-91%), and 0.96 (95% CI 0.93-0.97), respectively. Some studies demonstrated that DL models could achieve diagnostic performance comparable with, or even exceeding, that of clinicians in certain scenarios. The application of transfer learning contributed to improved model performance. DL algorithms exhibit promising diagnostic accuracy in TN imaging, highlighting their potential as auxiliary diagnostic tools. However, current studies are limited by suboptimal methodological design, inconsistent image quality across datasets, and insufficient external validation, which may introduce bias. Future research should enhance methodological standardization, improve model interpretability, and promote transparent reporting to facilitate the sustainable clinical translation of DL-based solutions.

Ultrasound imaging systems rely on accurate point spread function (PSF) estimation to support advanced image quality enhancement techniques such as deconvolution and speckle reduction. Phase aberration, caused by sound speed inhomogeneity within biological tissue, is inevitable in ultrasound imaging. It distorts the PSF by increasing sidelobe level and introducing asymmetric amplitude, making PSF estimation under phase aberration highly challenging. In this work, we propose a deep learning framework for estimating phase-aberrated PSFs using U-Net and complex U-Net architectures, operating on RF and complex k-space data, respectively, with the latter demonstrating superior performance. Synthetic phase aberration data, generated using the near-field phase screen model, is employed to train the networks. We evaluate various loss functions and find that log-compressed B-mode perceptual loss achieves the best performance, accurately predicting both the mainlobe and near sidelobe regions of the PSF. Simulation results validate the effectiveness of our approach in estimating PSFs under varying levels of phase aberration. Furthermore, we demonstrate that more accurate PSF estimation improves performance in a downstream phase aberration correction task, highlighting the broader utility of the proposed method.

Acute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium. This study examines ultrasomics-a novel cardiac ultrasound-based radiomics technique to extract high-throughput pixel-level information from images-for identifying ultrasonic texture and morphologic changes associated with infarcted myocardium. We included 684 participants from multisource data: a) a retrospective single-center matched case-control dataset, b) a prospective multicenter matched clinical trial dataset, and c) an open-source international and multivendor dataset. Handcrafted and deep transfer learning-based ultrasomics features from 2- and 4-chamber echocardiographic views were used to train machine learning (ML) models with the use of leave-one-source-out cross-validation for external validation. The ML model showed a higher AUC than transfer learning-based deep features in identifying MI [AUCs: 0.87 [95% CI: 0.84-0.89] vs 0.74 [95% CI: 0.70-0.77]; P < 0.0001]. ML probability was an independent predictor of MI even after adjusting for conventional echocardiographic parameters [adjusted OR: 1.03 [95% CI: 1.01-1.05]; P < 0.0001]. ML probability showed diagnostic value in differentiating acute MI, even in the presence of myocardial dysfunction (averaged longitudinal strain [LS] <16%) (AUC: 0.84 [95% CI: 0.77-0.89]). In addition, combining averaged LS with ML probability significantly improved predictive performance compared with LS alone (AUCs: 0.86 [95% CI: 0.80-0.91] vs 0.80 [95% CI: 0.72-0.87]; P = 0.02). Visualization of ultrasomics features with the use of a Manhattan plot discriminated infarcted and noninfarcted segments (P < 0.001) and facilitated parametric visualization of infarcted myocardium. This study demonstrates the potential of cardiac ultrasomics to distinguish healthy from infarcted myocardium and highlights the need for validation in diverse populations to define its role and incremental value in myocardial tissue characterization beyond conventional echocardiography.

To establish an ultrasound-based radiomics model to differentiate fibro adipose vascular anomaly (FAVA) and intramuscular venous malformation (VM). The clinical data of 65 patients with VM and 31 patients with FAVA who were treated and pathologically confirmed were retrospectively analyzed. Dimensionality reduction was performed on these features using the least absolute shrinkage and selection operator (LASSO). An ultrasound-based radiomics model was established using support vector machine (SVM) and random forest (RF) models. The diagnostic efficiency of this model was evaluated using the receiver operating characteristic. A total of 851 features were obtained by feature extraction, and 311 features were screened out using the <i>t</i>-test and Mann-Whitney <i>U</i> test. The dimensionality reduction was performed on the remaining features using LASSO. Finally, seven features were included to establish the diagnostic prediction model. In the testing group, the AUC, accuracy and specificity of the SVM model were higher than those of the RF model (0.841 [0.815-0.867] vs. 0.791 [0.759-0.824], 96.6% vs. 93.1%, and 100.0% vs. 90.5%, respectively). However, the sensitivity of the SVM model was lower than that of the RF model (88.9% vs. 100.0%). In this study, a prediction model based on ultrasound radiomics was developed to distinguish FAVA from VM. The study achieved high classification accuracy, sensitivity, and specificity. SVM model is superior to RF model and provides a new perspective and tool for clinical diagnosis.

Purpose: Prenatal ultrasound is a key tool in evaluating fetal structural development and detecting abnormalities, contributing to reduced perinatal complications and improved neonatal survival. Accurate identification of standard fetal torso planes is essential for reliable assessment and personalized prenatal care. However, limitations such as low contrast and unclear texture details in ultrasound imaging pose significant challenges for fine-grained anatomical recognition. Methods: We propose a novel Multi-Contrast Fusion Module (MCFM) to enhance the model's ability to extract detailed information from ultrasound images. MCFM operates exclusively on the lower layers of the neural network, directly processing raw ultrasound data. By assigning attention weights to image representations under different contrast conditions, the module enhances feature modeling while explicitly maintaining minimal parameter overhead. Results: The proposed MCFM was evaluated on a curated dataset of fetal torso plane ultrasound images. Experimental results demonstrate that MCFM substantially improves recognition performance, with a minimal increase in model complexity. The integration of multi-contrast attention enables the model to better capture subtle anatomical structures, contributing to higher classification accuracy and clinical reliability. Conclusions: Our method provides an effective solution for improving fetal torso plane recognition in ultrasound imaging. By enhancing feature representation through multi-contrast fusion, the proposed approach supports clinicians in achieving more accurate and consistent diagnoses, demonstrating strong potential for clinical adoption in prenatal screening. The codes are available at https://github.com/sysll/MCFM.

Accurate segmentation of thyroid nodules in ultrasound images is critical for clinical diagnosis but remains challenging due to low contrast and complex anatomical structures. Existing deep learning methods often rely solely on local nodule features, lacking anatomical prior knowledge of the thyroid region, which can result in misclassification of non-thyroid tissues, especially in low-quality scans. To address these issues, we propose a Spatial Prior-Guided Dual-Path Network that integrates a prior-aware encoder to model thyroid anatomical structures and a low-cost heterogeneous encoder to preserve fine-grained multi-scale features, enhancing both spatial detail and contextual awareness. To capture the diverse and irregular appearances of nodules, we design a CrossBlock module, which combines an efficient cross-attention mechanism with mixed-scale convolutional operations to enable global context modeling and local feature extraction. The network further employs a dual-decoder architecture, where one decoder learns thyroid region priors and the other focuses on accurate nodule segmentation. Gland-specific features are hierarchically refined and injected into the nodule decoder to enhance boundary delineation through anatomical guidance. Extensive experiments on the TN3K and MTNS datasets demonstrate that our method consistently outperforms state-of-the-art approaches, particularly in boundary precision and localization accuracy, offering practical value for preoperative planning and clinical decision-making.

BackgroundKawasaki disease (KD) is an acute, pediatric vasculitis associated with coronary artery abnormality (CAA) development. Echocardiography at month 1 post-diagnosis remains the standard for CAA surveillance despite limitations, including patient distress and increased healthcare burden. With declining CAA incidence due to improved treatment, the need for routine follow-up imaging is being reconsidered. This study aimed to develop and externally validate models for predicting CAA development and guide the need for echocardiography. MethodsThis study used two prospective multicenter Japanese registries: PEACOCK for model development and internal validation, and Post-RAISE for external validation. The primary outcome was CAA at the month 1 follow-up, defined as a maximum coronary artery Z score (Zmax) [&ge;] 2. Twenty-nine clinical, laboratory, echocardiographic, and treatment-related variables obtained within one week of diagnosis were selected as predictors. The models included simple models using the previous Zmax as a single predictor, logistic regression models, and machine learning models (LightGBM and XGBoost). Their discrimination, calibration, and clinical utility were assessed. ResultsAfter excluding patients without outcome data, 4,973 and 2,438 patients from PEACOCK and Post-RAISE, respectively, were included. The CAA incidence at month 1 was 5.5% and 6.8% for the respective group. For external validation, a simple model using the Zmax at week 1 produced an area under the curve of 0.79, which failed to improve by more than 0.02 after other variables were added or more complex models were used. Even the best-performing models with a highly sensitive threshold failed to reduce the need for echocardiography at month 1 by more than 30% while maintaining the number of undiagnosed CAA cases to less than ten. The predictive performance declined considerably when the Zmax was omitted from the multivariable models. ConclusionsThe Zmax at week 1 was the strongest predictor of CAA at month 1 post-diagnosis. Even advanced models incorporating additional variables failed to achieve a clinically acceptable trade-off between reducing the need for echocardiography and reducing the number of undiagnosed CAA cases. Until superior predictors are identified, echocardiography at month 1 should remain the standard practice. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIThe maximum Z score on echocardiography one week after diagnosis was the strongest of 29 variables for predicting coronary artery abnormalities (CAA) in patients with Kawasaki disease. C_LIO_LIEven the most sensitive models had a suboptimal ability to predict CAA development and reduce the need for imaging studies, suggesting they have limited utility in clinical decision-making. C_LI What Are the Clinical Implications?O_LIUntil more accurate predictors are found or imaging strategies are optimized, performing echocardiography at one-month follow-up should remain the standard of care. C_LI

<b>Objective:</b> To develop a machine learning diagnostic model for T-shaped uterus based on quantitative parameters from 3D transvaginal ultrasound. <b>Methods:</b> A retrospective cross-sectional study was conducted, recruiting 304 patients who visited the hysteroscopy centre of Fuxing Hospital, Beijing, China, between July 2021 and June 2024 for reasons such as "infertility or recurrent pregnancy loss" and other adverse obstetric histories. Twelve experts, including seven clinicians and five sonographers, from Fuxing Hospital and Beijing Obstetrics and Gynecology Hospital of Capital Medical University, Peking University People's Hospital, and Beijing Hospital, independently and anonymously assessed the diagnosis of T-shaped uterus using a modified Delphi method. Based on the consensus results, 56 cases were classified into the T-shaped uterus group and 248 cases into the non-T-shaped uterus group. A total of 7 clinical features and 14 sonographic features were initially included. Features demonstrating significant diagnostic impact were selected using 10-fold cross-validated LASSO (Least Absolute Shrinkage and Selection Operator) regression. Four machine learning algorithms [logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM)] were subsequently implemented to develop T-shaped uterus diagnostic models. Using the Python random module, the patient dataset was randomly divided into five subsets, each maintaining the original class distribution (T-shaped uterus: non-T-shaped uterus ≈ 1∶4) and a balanced number of samples between the two categories. Five-fold cross-validation was performed, with four subsets used for training and one for validation in each round, to enhance the reliability of model evaluation. Model performance was rigorously assessed using established metrics: area under the curve (AUC) of receiver operator characteristic (ROC) curve, sensitivity, specificity, precision, and F1-score. In the RF model, feature importance was assessed by the mean decrease in Gini impurity attributed to each variable. <b>Results:</b> A total of 304 patients had a mean age of (35±4) years, and the age of the T-shaped uterus group was (35±5) years; the age of the non-T-shaped uterus group was (34±4) years.. Eight features with non-zero coefficients were selected by LASSO regression, including average lateral wall indentation width, average lateral wall indentation angle, upper cavity depth, endometrial thickness, uterine cavity area, cavity width at level of lateral wall indentation, angle formed by the bilateral lateral walls, and average cornual angle (coefficient: 0.125, -0.064,-0.037,-0.030,-0.026,-0.025,-0.025 and -0.024, respectively). The RF model showed the best diagnostic performance: in training set, AUC was 0.986 (95%<i>CI</i>: 0.980-0.992), sensitivity was 0.978, specificity 0.946, precision 0.802, and F1-score 0.881; in testing set, AUC was 0.948 (95%<i>CI</i>: 0.911-0.985), sensitivity was 0.873, specificity 0.919, precision 0.716, and F1-score 0.784. RF model feature importance analysis revealed that average lateral wall indentation width, upper cavity depth, and average lateral wall indentation angle were the top three features (over 65% in total), playing a decisive role in model prediction. <b>Conclusion:</b> The machine learning models developed in this study, particularly the RF model, are promising for the diagnosis of T-shaped uterus, offering new perspectives and technical support for clinical practice.