This study aimed to develop a Deep Learning Radiomics integrated model (DLRN), which combines photoacoustic/ultrasound(PA/US)imaging with clinical and radiomics features to distinguish between luminal and non-luminal BC in a preoperative setting. A total of 388 BC patients were included, with 271 in the training group and 117 in the testing group. Radiomics and deep learning features were extracted from PA/US images using Pyradiomics and ResNet50, respectively. Feature selection was performed using independent sample t-tests, Pearson correlation analysis, and LASSO regression to build a Deep Learning Radiomics (DLR) model. Based on the results of univariate and multivariate logistic regression analyses, the DLR model was combined with valuable clinical features to construct the DLRN model. Model efficacy was assessed using AUC, accuracy, sensitivity, specificity, and NPV. The DLR model comprised 3 radiomic features and 6 deep learning features, which, when combined with significant clinical predictors, formed the DLRN model. In the testing set, the AUC of the DLRN model (0.924 [0.877-0.972]) was significantly higher than that of the DLR (AUC 0.847 [0.758-0.936], p = 0.026), DL (AUC 0.822 [0.725-0.919], p = 0.06), Rad (AUC 0.717 [0.597-0.838], p < 0.001), and clinical (AUC 0.820 [0.745-0.895], p = 0.002) models. These findings indicate that the DLRN model (integrated model) exhibited the most favorable predictive performance among all models evaluated. The DLRN model effectively integrates PA/US imaging with clinical data, showing potential for preoperative molecular subtype prediction and guiding personalized treatment strategies for BC patients.

Ultrasound examination using the Graf method is widely applied for early detection of developmental dysplasia of the hip (DDH), but intra- and inter-operator variability remains a limitation. This study aimed to quantify operator variability in hip ultrasound assessments and to validate an AI-assisted system for automated α-angle measurement to improve reproducibility. Thirty participants of different experience levels, including trained clinicians, residents, and medical students, each performed six ultrasound scans on a standardized infant hip phantom. Examination time, iliac margin inclination, and α-angle measurements were analyzed to assess intra- and inter-operator variability. In parallel, an AI-based system was developed to automatically detect anatomical landmarks and calculate α-angles from static images and dynamic video sequences. Validation was conducted using the phantom model with a known α-angle of 70°. Clinicians achieved shorter examination times and higher reproducibility than residents and students, with manual measurements systematically underestimating the reference α-angle. Static AI produced closer estimates with greater variability, whereas dynamic AI achieved the highest accuracy (mean 69.2°) and consistency with narrower limits of agreement than manual measurements. These findings confirm substantial operator variability and demonstrate that AI-assisted dynamic ultrasound analysis can improve reproducibility and reliability in routine DDH screening.

Reliable out-of-distribution (OOD) detection is important for safe deployment of deep learning models in fetal ultrasound amidst heterogeneous image characteristics and clinical settings. OOD detection relies on estimating a classification model's uncertainty, which should increase for OOD samples. While existing research has largely focused on uncertainty quantification methods, this work investigates the impact of the classification task itself. Through experiments with eight uncertainty quantification methods across four classification tasks, we demonstrate that OOD detection performance significantly varies with the task, and that the best task depends on the defined ID-OOD criteria; specifically, whether the OOD sample is due to: i) an image characteristic shift or ii) an anatomical feature shift. Furthermore, we reveal that superior OOD detection does not guarantee optimal abstained prediction, underscoring the necessity to align task selection and uncertainty strategies with the specific downstream application in medical image analysis.

The objective was to develop an artificial intelligence (AI)-based system, using deep neural network (DNN) technology, to automatically detect standard fetal planes during video capture, measure fetal biometry parameters and estimate fetal weight. A standard plane recognition DNN was trained to classify ultrasound images into four categories: head circumference (HC), abdominal circumference (AC), femur length (FL) standard planes, or 'other'. The recognized standard plane images were subsequently processed by three fetal biometry DNNs, automatically measuring HC, AC and FL. Fetal weight was then estimated with the Hadlock 3 formula. The training dataset consisted of 16,626 images. A prospective temporal validation was then conducted using an independent set of 281 ultrasound videos of healthy fetuses. Fetal weight and biometry measurements were compared against an expert sonographer. Two less experienced sonographers were used as controls. The AI system obtained a significantly lower absolute relative measurement error in fetal weight estimation than the controls (AI vs. medium-level: p = 0.032, AI vs. beginner: p < 1e-8), so in AC measurements (AI vs. medium-level: p = 1.72e-04, AI vs. beginner: p < 1e-06). Average absolute relative measurement errors of AI versus expert were: 0.96 % (S.D. 0.79 %) for HC, 1.56 % (S.D. 1.39 %) for AC, 1.77 % (S.D. 1.46 %) for FL and 3.10 % (S.D. 2.74 %) for fetal weight estimation. The AI system produced similar biometry measurements and fetal weight estimation to those of the expert sonographer. It is a promising tool to enhance non-expert sonographers' performance and reproducibility in fetal biometry measurements, and to reduce inter-operator variability.

This systematic review evaluates advanced ultrasound quantitative techniques including contrast-enhanced ultrasound, elastography, quantitative ultrasound (QUS), multiparametric ultrasound, and artificial intelligence for characterizing focal liver lesions (FLLs). It critically appraises their technical principles, parameter extraction methodologies, and clinical validation frameworks. It further integrates and comparatively analyzes their diagnostic performance across major FLL subtypes, including hepatocellular carcinoma, metastases, hemangioma, and focal nodular hyperplasia. This work provides a foundation for improving noninvasive FLL diagnosis and highlights the imperative for standardization and clinical translation of advanced QUS in hepatology.

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, and echocardiography is critical for diagnosis of both common and congenital cardiac conditions. However, echocardiographic data for certain pathologies are scarce, hindering the development of robust automated diagnosis models. In this work, we propose Echo-Path, a novel generative framework to produce echocardiogram videos conditioned on specific cardiac pathologies. Echo-Path can synthesize realistic ultrasound video sequences that exhibit targeted abnormalities, focusing here on atrial septal defect (ASD) and pulmonary arterial hypertension (PAH). Our approach introduces a pathology-conditioning mechanism into a state-of-the-art echo video generator, allowing the model to learn and control disease-specific structural and motion patterns in the heart. Quantitative evaluation demonstrates that the synthetic videos achieve low distribution distances, indicating high visual fidelity. Clinically, the generated echoes exhibit plausible pathology markers. Furthermore, classifiers trained on our synthetic data generalize well to real data and, when used to augment real training sets, it improves downstream diagnosis of ASD and PAH by 7\% and 8\% respectively. Code, weights and dataset are available here https://github.com/Marshall-mk/EchoPathv1

This study aimed to analyze the correlation between the ultrasonographic radiomic features of multiple regions within and surrounding the primary tumor in breast cancer patients prior to receiving neoadjuvant chemotherapy (NAC) and the efficacy of NAC. By integrating clinical and pathological parameters, a predictive model was constructed to provide an accurate basis for personalized treatment and precise prognosis in breast cancer patients. This retrospective study included 321 breast cancer patients who underwent NAC treatment at the Second Hospital of Lanzhou University from January 2019 to December 2024. According to post-operative pathological results, the patients were divided into pathological complete response (PCR) and non-pathological complete response (non-PCR) groups. Regions of interest were outlined on 2-D ultrasound images using Itk-snap software. The intra-tumor (Intra) region and 5 mm (Peri-5 mm), 10 mm (Peri-10 mm) and 15 mm (Peri-15 mm) the peri-tumoralregions were demarcated, with radiomics features extracted from each region. Patients were randomly divided into a training set (n = 224) and a validation set (n = 97) in a 7:3 ratio. All features underwent Z-score normalization followed by dimensionality reduction using t-tests, Pearson correlation coefficients and least absolute shrinkage and selection operator. Radiomics models for Intra, Peri-5 mm, Peri-10 mm, Peri-15 mm and the combined intra-tumoral and peri-tumoral regions (Intra-tumoral, Peri-tumoral, IntraPeri) were constructed using a random forest machine-learning classifier. The predictive performance of the models was assessed by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Additionally, calibration curves and decision curve analysis were plotted to evaluate the model's goodness of fit and clinical net benefit RESULTS: A total of 214 radiomics features were extracted from the intra-tumoral and multi-region peri-tumoral areas. Using the least absolute shrinkage and selection operator regression model, eight intra-tumoral radiomics features, eight peri-10 mm radiomics features and nine IntraPeri-10 mm radiomics features were selected as being closely associated with PCR. The AUC of the intra-tumoral model was 0.860 and 0.823 in the training and validation sets, respectively. The AUCs of the peri-5 mm, Peri-10 mm and Peri-15 mm models were 0.836, 0.854 and 0.822 in the training set, and 0.793, 0.799 and 0.792 in the validation set. Among them, the AUC of the IntraPeri-10 mm model in the validation set was 0.842 (95% confidence interval [CI]: 0.764-0.921), which was superior to the AUC of the IntraPeri-5 mm model (0.831; 95% CI: 0.758-0.914) and the IntraPeri-15 mm model (0.838; 95% CI: 0.761-0.917). The combined model based on IntraPeri-10 mm and clinical pathological parameters (HER-2, Ki-67) achieved an AUC of 0.869 (95% CI: 0.800-0.937). The Delong test showed that the AUC of the combined model was significantly superior to that of the other models. The calibration curve indicated that the combined model had a good fit, and decision curve analysis demonstrated that the combined model provided a better clinical net benefit. The peri-10 mm region is the optimal predictive area for the tumor's surrounding tissue after NAC in breast cancer. The IntraPeri-10 mm model, incorporating clinical pathological parameters, performs better at predicting the efficacy of NAC in breast cancer and can accurately assess treatment response, offering valuable guidance for subsequent treatment decisions.

Lung ultrasound (LUS) has been widely used by point-of-care systems in both children and adult populations to provide different clinical diagnostics. This research aims to develop an interpretable system that uses a deep fusion network for classifying LUS video/patients based on extracted features by using texture analysis and transfer learning techniques to assist physicians. The pulmonary edema dataset includes 56 LUS videos and 4234 LUS frames. The COVID-BLUES dataset includes 294 LUS videos and 15,826 frames. The proposed multi-feature fusion classification network (MFFC-Net) includes the following: (1) two features extracted from Inception-ResNet-v2, Inception-v3, and 9 texture features of gray-level co-occurrence matrix (GLCM) and histogram of the region of interest (ROI); (2) a neural network for classifying LUS images with feature fusion input; and (3) four models (i.e., ANN, SVM, XGBoost, and kNN) used for classifying COVID/NON COVID patients. The training process was evaluated based on accuracy (0.9969), F1-score (0.9968), sensitivity (0.9967), specificity (0.9990), and precision (0.9970) metrics after the fivefold cross-validation stage. The results of the ANOVA analysis with 9 features of LUS images show that there was a significant difference between pulmonary edema and normal lungs (p < 0.01). The test results at the frame level of the MFFC-Net model achieved an accuracy of 100% and ROC-AUC (1.000) compared with ground truth at the video level with 4 groups of LUS videos. Test results at the patient level with the COVID-BLUES dataset achieved the highest accuracy of 81.25% with the kNN model. The proposed MFFC-Net model has 125 times higher information density (ID) compared to Inception-ResNet-v2 and 53.2 times compared with Inception-v3.

In this study, we develop a new CAD system for accurate thyroid cancer classification with emphasis on feature extraction. Prior studies have shown that thyroid texture is important for segregating the thyroid ultrasound images into different classes. Based upon our experience with breast cancer classification, we first conjuncture that the Discrete Cosine Transform (DCT) is the best descriptor for capturing textural features. Thyroid ultrasound images are particularly challenging as the gland is surrounded by multiple complex anatomical structures leading to variations in tissue density. Hence, we second conjuncture the importance of localization and propose that the Local DCT (LDCT) descriptor captures the textural features best in this context. Another disadvantage of complex anatomy around the thyroid gland is scattering of ultrasound waves resulting in noisy and unclear textures. Hence, we third conjuncture that one image descriptor is not enough to fully capture the textural features and propose the integration of another popular texture capturing descriptor (Improved Local Binary Pattern, ILBP) with LDCT. ILBP is known to be noise resilient as well. We term our novel descriptor as Binary Pattern Driven Local Discrete Cosine Transform (BPD-LDCT). Final classification is carried out using a non-linear SVM. The proposed CAD system is evaluated on the only two publicly available thyroid cancer datasets, namely TDID and AUITD. The evaluation is conducted in two stages. In Stage I, thyroid nodules are categorized as benign or malignant. In Stage II, the malignant cases are further sub-classified into TI-RADS (4) and TI-RADS (5). For Stage I classification, our proposed model demonstrates exceptional performance of nearly 100% on TDID and 97% on AUITD. In Stage II classification, the proposed model again attains excellent classification of close to 100% on TDID and 99% on AUITD.

One in 25 deaths worldwide is related to liver disease, and often with multiple hepatosplenic conditions. Yet, little is understood of the risk factors for hepatosplenic multimorbidity, especially in the context of chronic infections. We present a novel Bayesian multitask learning framework to jointly model 45 hepatosplenic conditions assessed using point-of-care B-mode ultrasound for 3155 individuals aged 5-91 years within the SchistoTrack cohort across rural Uganda where chronic intestinal schistosomiasis is endemic. We identified distinct and shared biomedical, socioeconomic, and spatial risk factors for individual conditions and hepatosplenic multimorbidity, and introduced methods for measuring condition dependencies as risk factors. Notably, for gastro-oesophageal varices, we discovered key risk factors of older age, lower hemoglobin concentration, and severe schistosomal liver fibrosis. Our findings provide a compendium of risk factors to inform surveillance, triage, and follow-up, while our model enables improved prediction of hepatosplenic multimorbidity, and if validated on other systems, general multimorbidity.