Ultrasound assessment of fetal size and growth is the mainstay of monitoring fetal well-being during pregnancy, as being small for gestational age (SGA) or large for gestational age (LGA) poses significant risks for both the fetus and the mother. This study aimed to enhance the prediction accuracy of abnormal fetal growth. We developed a deep learning model, trained on a dataset of 433,096 ultrasound images derived from 94,538 examinations conducted on 65,752 patients. The deep learning model performed significantly better in detecting both SGA (58% vs 70%) and LGA compared with the current clinical standard, the Hadlock formula (41% vs 55%), p < 0.001. Additionally, the model estimates were significantly less biased across all demographic and technical variables compared to the Hadlock formula. Incorporating key anatomical features such as cortical structures, liver texture, and skin thickness was likely to be responsible for the improved prediction accuracy observed.

To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinical staging of cervical cancer. General clinical and ultrasound data of 227 patients with cervical cancer who received transvaginal ultrasonography were retrospectively analyzed. The region of interest (ROI) radiomics profiles of the original image and derived image were retrieved and profile screening was performed. The chosen profiles were employed in radiomics model and Radscore formula construction. Prediction models were developed utilizing several ML algorithms by Python based on an integrated dataset of clinical features and ultrasound radiomics. Model performances were evaluated via AUC. Plot calibration curves and clinical decision curves were used to assess model efficacy. The model developed by support vector machine (SVM) emerged as the superior model. Integrating clinical characteristics with ultrasound radiomics, it showed notable performance metrics in both the training and validation datasets. Specifically, in the training set, the model obtained an AUC of 0.88 (95% Confidence Interval (CI): 0.83-0.93), alongside a 0.84 accuracy, 0.68 sensitivity, and 0.91 specificity. When validated, the model maintained an AUC of 0.77 (95% CI: 0.63-0.88), with 0.77 accuracy, 0.62 sensitivity, and 0.83 specificity. The calibration curve aligned closely with the perfect calibration line. Additionally, based on the clinical decision curve analysis, the model offers clinical utility over wide-ranging threshold possibilities. The clinical- and radiomics-based SVM model provides a noninvasive tool for predicting cervical cancer stage, integrating ultrasound radiomics and key clinical factors (age, abortion history) to improve risk stratification. This approach could guide personalized treatment (surgery vs. chemoradiation) and optimize staging accuracy, particularly in resource-limited settings where advanced imaging is scarce.

The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is accurate, it has invasive risks and high economic costs, which are difficult for some patients to accept. Therefore, this study uses deep learning technology to build a liver fibrosis diagnosis model to achieve non-invasive staging of liver fibrosis, avoid complications, and reduce costs. This study uses ultrasound examination to obtain pure liver parenchyma image section data. With the consent of the patient, combined with the results of percutaneous liver puncture biopsy, the degree of liver fibrosis indicated by ultrasound examination data is judged. The concept of Fibrosis Contrast Layer (FCL) is creatively introduced in our experimental method, which can help our model more keenly capture the significant differences in the characteristics of liver fibrosis of various grades. Finally, through label fusion (LF), the characteristics of liver specimens of the same fibrosis stage are abstracted and fused to improve the accuracy and stability of the diagnostic model. Experimental evaluation demonstrated that our model achieved an accuracy of 85.6%, outperforming baseline models such as ResNet (81.9%), InceptionNet (80.9%), and VGG (80.8%). Even under a small-sample condition (30% data), the model maintained an accuracy of 84.8%, significantly outperforming traditional deep-learning models exhibiting sharp performance declines. The training results show that in the whole sample data set and 30% small sample data set training environments, the FCLLF model's test performance results are better than those of traditional deep learning models such as VGG, ResNet, and InceptionNet. The performance of the FCLLF model is more stable, especially in the small sample data set environment. Our proposed FCLLF model effectively improves the accuracy and stability of liver fibrosis staging using non-invasive ultrasound imaging.

BackgroundAcute appendicitis remains a challenging diagnosis in pediatric populations, with high rates of misdiagnosis and negative appendectomies despite advances in imaging modalities. Current diagnostic tools, including clinical scoring systems like Alvarado and Pediatric Appendicitis Score (PAS), lack sufficient sensitivity and specificity, while reliance on CT scans raises concerns about radiation exposure, contrast hazards and sedation in children. Moreover, no established tool effectively predicts progression from uncomplicated to complicated appendicitis, creating a critical gap in clinical decision-making. ObjectiveTo develop and evaluate a machine learning model that integrates clinical, laboratory, and radiological findings for accurate diagnosis and complication prediction in pediatric appendicitis and to deploy this model as an interpretable web-based tool for clinical decision support. MethodsWe analyzed data from 780 pediatric patients (ages 0-18) with suspected appendicitis admitted to Childrens Hospital St. Hedwig, Regensburg, between 2016 and 2021. For severity prediction, our dataset was augmented with 430 additional cases from published literature and only the confirmed cases of acute appendicitis(n=602) were used. After feature selection using statistical methods and recursive feature elimination, we developed a Random Forest model named Dharma, optimized through hyperparameter tuning and cross-validation. Model performance was evaluated on independent test sets and compared with conventional diagnostic tools. ResultsDharma demonstrated superior diagnostic performance with an AUC-ROC of 0.96 ({+/-}0.02 SD) in cross-validation and 0.97-0.98 on independent test sets. At an optimal threshold of 64%, the model achieved specificity of 88%-98%, sensitivity of 89%-95%, and positive predictive value of 93%-99%. For complication prediction, Dharma attained a sensitivity of 93% ({+/-}0.05 SD) in cross-validation and 96% on the test set, with a negative predictive value of 98%. The model maintained strong performance even in cases where the appendix could not be visualized on ultrasonography (AUC-ROC 0.95, sensitivity 89%, specificity 87% at the threshold of 30%). ConclusionDharma is a novel, interpretable machine learning based clinical decision support tool designed to address the diagnostic challenges of pediatric appendicitis by integrating easily obtainable clinical, laboratory, and radiological data into a unified, real-time predictive framework. Unlike traditional scoring systems and imaging modalities, which may lack specificity or raise safety concerns in children, Dharma demonstrates high accuracy in diagnosing appendicitis and predicting progression from uncomplicated to complicated cases, potentially reducing unnecessary surgeries and CT scans. Its robust performance, even with incomplete imaging data, underscores its utility in resource-limited settings. Delivered through an intuitive, transparent, and interpretable web application, Dharma supports frontline providers--particularly in low- and middle-income settings--in making timely, evidence-based decisions, streamlining patient referrals, and improving clinical outcomes. By bridging critical gaps in current diagnostic and prognostic tools, Dharma offers a practical and accessible 21st-century solution tailored to real-world pediatric surgical care across diverse healthcare contexts. Furthermore, the underlying framework and concepts of Dharma may be adaptable to other clinical challenges beyond pediatric appendicitis, providing a foundation for broader applications of machine learning in healthcare. Author SummaryAccurate diagnosis of pediatric appendicitis remains challenging, with current clinical scores and imaging tests limited by sensitivity, specificity, predictive values, and safety concerns. We developed Dharma, an interpretable machine learning model that integrates clinical, laboratory, and radiological data to assist in diagnosing appendicitis and predicting its severity in children. Evaluated on a large dataset supplemented by published cases, Dharma demonstrated strong diagnostic and prognostic performance, including in cases with incomplete imaging--making it potentially especially useful in resource-limited settings for early decision-making and streamlined referrals. Available as a web-based tool, it provides real-time support to healthcare providers in making evidence-based decisions that could reduce negative appendectomies while avoiding hazards associated with advanced imaging modalities such as sedation, contrast, or radiation exposure. Furthermore, the open-access concepts and framework underlying Dharma have the potential to address diverse healthcare challenges beyond pediatric appendicitis.

Ultrasound fetal imaging is beneficial to support prenatal development because it is affordable and non-intrusive. Nevertheless, fetal plane classification (FPC) remains challenging and time-consuming for obstetricians since it depends on nuanced clinical aspects, which increases the difficulty in identifying relevant features of the fetal anatomy. Thus, to assist with its accurate feature extraction, a lightweight artificial intelligence architecture leveraging convolutional neural networks and attention mechanisms is proposed to classify the largest benchmark ultrasound dataset. The approach fine-tunes from lightweight EfficientNet feature extraction backbones pre-trained on the ImageNet1k. to classify key fetal planes such as the brain, femur, thorax, cervix, and abdomen. Our methodology incorporates the attention mechanism to refine features and 3-layer perceptrons for classification, achieving superior performance with the highest Top-1 accuracy of 96.25%, Top-2 accuracy of 99.80% and F1-Score of 0.9576. Importantly, the model has 40x fewer trainable parameters than existing benchmark ensemble or transformer pipelines, facilitating easy deployment on edge devices to help clinical practitioners with real-time FPC. The findings are also interpreted using GradCAM to carry out clinical correlation to aid doctors with diagnostics and improve treatment plans for expectant mothers.

Despite growing clinical use of contrast-enhanced ultrasound (CEUS), inconsistency remains in the modality's role in clinical pathways for hepatocellular carcinoma (HCC) diagnosis and management. This AJR Expert Panel Narrative Review provides practical insights on the use of CEUS for the diagnosis of HCC across populations, including individuals at high risk for HCC, individuals with metabolic dysfunction-associated steatotic liver disease, and remaining individuals not at high risk for HCC. Considerations addressed with respect to high-risk patients include CEUS diagnostic criteria for HCC, use of CEUS for differentiating HCC from non-HCC malignancy, use of CEUS for small (≤2 cm) lesions, use of CEUS for characterizing occult lesions on B-mode ultrasound, and use of CEUS for indeterminate lesions on CT or MRI. Representative literature addressing the use of CEUS for HCC diagnosis as well as gaps in knowledge requiring further investigation are highlighted. Throughout these discussions, the article distinguishes two broad types of ultrasound contrast agents used for liver imaging: pure blood-pool agents and a combined blood-pool and Kupffer-cell agent. Additional topics include the use of CEUS for treatment response assessment after nonradiation therapies and implications of artificial intelligence technologies. The article concludes with a series of consensus statements from the author panel.

Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the majority of the dose is delivered postoperatively with no deformation, the dosimetry approved at the time of implant may not accurately represent the dose delivered to the target and organs at risk. We aimed to evaluate the biological effect of the prostate deformation and its correlation with patient-reported outcomes. We prospectively acquired ultrasound images of the prostate pre- and postprobe cover inflation for 27 patients undergoing I-125 seed implant. The coordinates of implanted seeds from approved clinical plan were transferred to deformation-corrected prostate to simulate the actual dosimetry using a machine learning-based deformable image registration. The DVHs of both sets of plans were reduced to biologically effective dose (BED) distribution and subsequently to Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) metrics. The change in fourteen patient-reported rectal and urinary symptoms between pretreatment to 6 months post-op time points were correlated with the TCP and NTCP metrics using the area under the curve (AUC) and odds ratio (OR). Between the clinical and the deformation corrected research plans, the mean TCP decreased by 9.4% (p < 0.01), whereas mean NTCP of rectum decreased by 10.3% and that of urethra increased by 16.3%, respectively (p < 0.01). For the diarrhea symptom, the deformation corrected research plans showed AUC=0.75 and OR = 8.9 (1.3-58.8) for the threshold NTCP>20%, while the clinical plan showed AUC=0.56 and OR = 1.4 (0.2 to 9.0). For the symptom of urinary control, the deformation corrected research plans showed AUC = 0.70, OR = 6.9 (0.6 to 78.0) for the threshold of NTCP>15%, while the clinical plan showed AUC = 0.51 and no positive OR. Taking organ deformation into consideration, clinical brachytherapy plans showed worse tumor coverage, worse urethra sparing but better rectal sparing. The deformation corrected research plans showed a stronger correlation with the patient-reported outcome than the clinical plans for the symptoms of diarrhea and urinary control.

Three-dimensional ultrasound enables real-time volumetric visualization of anatomical structures. Unlike traditional 2D ultrasound, 3D imaging reduces the reliance on precise probe orientation, potentially making ultrasound more accessible to clinicians with varying levels of experience and improving automated measurements and post-exam analysis. However, achieving both high volume rates and high image quality remains a significant challenge. While 3D diverging waves can provide high volume rates, they suffer from limited tissue harmonic generation and increased multipath effects, which degrade image quality. One compromise is to retain the focusing in elevation while leveraging unfocused diverging waves in the lateral direction to reduce the number of transmissions per elevation plane. Reaching the volume rates achieved by full 3D diverging waves, however, requires dramatically undersampling the number of elevation planes. Subsequently, to render the full volume, simple interpolation techniques are applied. This paper introduces a novel approach to 3D ultrasound reconstruction from a reduced set of elevation planes by employing diffusion models (DMs) to achieve increased spatial and temporal resolution. We compare both traditional and supervised deep learning-based interpolation methods on a 3D cardiac ultrasound dataset. Our results show that DM-based reconstruction consistently outperforms the baselines in image quality and downstream task performance. Additionally, we accelerate inference by leveraging the temporal consistency inherent to ultrasound sequences. Finally, we explore the robustness of the proposed method by exploiting the probabilistic nature of diffusion posterior sampling to quantify reconstruction uncertainty and demonstrate improved recall on out-of-distribution data with synthetic anomalies under strong subsampling.

Background and objective: Micro-ultrasound (micro-US) is a novel imaging modality with diagnostic accuracy comparable to MRI for detecting clinically significant prostate cancer (csPCa). We investigated whether artificial intelligence (AI) interpretation of micro-US can outperform clinical screening methods using PSA and digital rectal examination (DRE). Methods: We retrospectively studied 145 men who underwent micro-US guided biopsy (79 with csPCa, 66 without). A self-supervised convolutional autoencoder was used to extract deep image features from 2D micro-US slices. Random forest classifiers were trained using five-fold cross-validation to predict csPCa at the slice level. Patients were classified as csPCa-positive if 88 or more consecutive slices were predicted positive. Model performance was compared with a classifier using PSA, DRE, prostate volume, and age. Key findings and limitations: The AI-based micro-US model and clinical screening model achieved AUROCs of 0.871 and 0.753, respectively. At a fixed threshold, the micro-US model achieved 92.5% sensitivity and 68.1% specificity, while the clinical model showed 96.2% sensitivity but only 27.3% specificity. Limitations include a retrospective single-center design and lack of external validation. Conclusions and clinical implications: AI-interpreted micro-US improves specificity while maintaining high sensitivity for csPCa detection. This method may reduce unnecessary biopsies and serve as a low-cost alternative to PSA-based screening. Patient summary: We developed an AI system to analyze prostate micro-ultrasound images. It outperformed PSA and DRE in detecting aggressive cancer and may help avoid unnecessary biopsies.

Early detection of malignant lesions in ultrasound images is crucial for effective cancer diagnosis and treatment. While traditional methods rely on radiologists, deep learning models can improve accuracy, reduce errors, and enhance efficiency. This study explores the application of a deep learning model for classifying benign and malignant lesions, focusing on its performance and interpretability. In this study, we proposed a feature fusion-based deep learning model for classifying benign and malignant lesions in ultrasound images. The model leverages advanced architectures such as MobileNetV2 and DenseNet121, enhanced with feature fusion and attention mechanisms to boost classification accuracy. The clinical dataset comprises 2171 images collected from 1758 patients between December 2020 and May 2024. Additionally, we utilized the publicly available BUSI dataset, consisting of 780 images from female patients aged 25 to 75, collected in 2018. To enhance interpretability, we applied Grad-CAM, Saliency Maps, and shapley additive explanations (SHAP) techniques to explain the model's decision-making. A comparative analysis with radiologists of varying expertise levels is also conducted. The proposed model exhibited the highest performance, achieving an AUC of 0.9320 on our private dataset and an area under the curve (AUC) of 0.9834 on the public dataset, significantly outperforming traditional deep convolutional neural network models. It also exceeded the diagnostic performance of radiologists, showcasing its potential as a reliable tool for medical image classification. The model's success can be attributed to its incorporation of advanced architectures, feature fusion, and attention mechanisms. The model's decision-making process was further clarified using interpretability techniques like Grad-CAM, Saliency Maps, and SHAP, offering insights into its ability to focus on relevant image features for accurate classification. The proposed deep learning model offers superior accuracy in classifying benign and malignant lesions in ultrasound images, outperforming traditional models and radiologists. Its strong performance, coupled with interpretability techniques, demonstrates its potential as a reliable and efficient tool for medical diagnostics. The datasets generated and analyzed during the current study are not publicly available due to the nature of this research and participants of this study, but may be available from the corresponding author on reasonable request.