Accurate diagnosis of pancreatic cystic lesions (PCLs) is essential to guide appropriate management and reduce unnecessary surgeries. Despite multiple guidelines in PCL management, a substantial proportion of patients still undergo major resections for benign cysts, and a majority of resected intraductal papillary mucinous neoplasms (IPMNs) show only low-grade dysplasia, leading to significant clinical, financial, and psychological burdens. This review highlights emerging endoscopic approaches that enhance diagnostic accuracy and support organ-sparing, minimally invasive management of PCLs. Recent studies suggest that endoscopic ultrasound (EUS) and its accessory techniques, such as contrast-enhanced EUS and needle-based confocal laser endomicroscopy, as well as next-generation sequencing analysis of cyst fluid, not only accurately characterize PCLs but are also well tolerated and cost-effective. Additionally, emerging therapeutics such as EUS-guided radiofrequency ablation (RFA) and EUS-chemoablation are promising as minimally invasive treatments for high-risk mucinous PCLs in patients who are not candidates for surgery. Accurate diagnosis of PCLs remains challenging, leading to many patients undergoing unnecessary surgery. Emerging endoscopic imaging biomarkers, artificial intelligence analysis, and molecular biomarkers enhance diagnostic precision. Additionally, novel endoscopic ablative therapies offer safe, minimally invasive, organ-sparing treatment options, thereby reducing the healthcare resource burdens associated with overtreatment.

Traditional echocardiographic parameters such as ejection fraction (EF) and global longitudinal strain (GLS) have limitations in the early detection of cardiac dysfunction. EF often remains normal despite underlying pathology, and GLS is influenced by load conditions and vendor variability. There is a growing need for reproducible, interpretable, and operator-independent parameters that capture subtle and global cardiac functional alterations. We introduce the Acoustic Index, a novel AI-derived echocardiographic parameter designed to quantify cardiac dysfunction from standard ultrasound views. The model combines Extended Dynamic Mode Decomposition (EDMD) based on Koopman operator theory with a hybrid neural network that incorporates clinical metadata. Spatiotemporal dynamics are extracted from echocardiographic sequences to identify coherent motion patterns. These are weighted via attention mechanisms and fused with clinical data using manifold learning, resulting in a continuous score from 0 (low risk) to 1 (high risk). In a prospective cohort of 736 patients, encompassing various cardiac pathologies and normal controls, the Acoustic Index achieved an area under the curve (AUC) of 0.89 in an independent test set. Cross-validation across five folds confirmed the robustness of the model, showing that both sensitivity and specificity exceeded 0.8 when evaluated on independent data. Threshold-based analysis demonstrated stable trade-offs between sensitivity and specificity, with optimal discrimination near this threshold. The Acoustic Index represents a physics-informed, interpretable AI biomarker for cardiac function. It shows promise as a scalable, vendor-independent tool for early detection, triage, and longitudinal monitoring. Future directions include external validation, longitudinal studies, and adaptation to disease-specific classifiers.

B-ultrasound results are widely used in early pregnancy loss (EPL) prediction, but there are inevitable intra-observer and inter-observer errors in B-ultrasound results especially in early pregnancy, which lead to inconsistent assessment of embryonic status, and thus affect the judgment of EPL. To address this, we need a rapid and accurate model to predict pregnancy loss in the first trimester. This study aimed to construct an artificial intelligence model to automatically extract biometric parameters from ultrasound videos of early embryos and predict pregnancy loss. This can effectively eliminate the measurement error of B-ultrasound results, accurately predict EPL, and provide decision support for doctors with relatively little clinical experience. A total of 630 ultrasound videos from women with early singleton pregnancies of gestational age between 6 and 10 weeks were used for training. A two-stage artificial intelligence model was established. First, some biometric parameters such as gestational sac areas (GSA), yolk sac diameter (YSD), crown rump length (CRL) and fetal heart rate (FHR), were extract from ultrasound videos by a deep neural network named A3F-net, which is a modified neural network based on U-Net designed by ourselves. Then an ensemble learning model predicted pregnancy loss risk based on these features. Dice, IOU and Precision were used to evaluate the measurement results, and sensitivity, AUC etc. were used to evaluate the predict results. The fetal heart rate was compared with those measured by doctors, and the accuracy of results was compared with other AI models. In the biometric features measurement stage, the precision of GSA, YSD and CRL of A3F-net were 98.64%, 96.94% and 92.83%, it was the highest compared to other 2 models. Bland-Altman analysis did not show systematic deviations between doctors and AI. The mean and standard deviation of the mean relative error between doctors and the AI model was 0.060 ± 0.057. In the EPL prediction stage, the ensemble learning models demonstrated excellent performance, with CatBoost being the best-performing model, achieving a precision of 98.0% and an AUC of 0.969 (95% CI: 0.962-0.975). In this study, a hybrid AI model to predict EPL was established. First, a deep neural network automatically measured the biometric parameters from ultrasound video to ensure the consistency and accuracy of the measurements, then a machine learning model predicted EPL risk to support doctors making decisions. The use of our established AI model in EPL prediction has the potential to assist physicians in making more accurate and timely clinical decision in clinical application.

Conservative treatment remains a viable option for selected patients with ectopic pregnancy (EP), but failure may lead to rupture and serious complications. Currently, serum β-hCG is the main predictor for treatment outcomes, yet its accuracy is limited. This study aimed to develop and validate a predictive model that integrates radiomic features derived from super-resolution (SR) ultrasound images with clinical biomarkers to improve risk stratification. A total of 228 patients with EP receiving conservative treatment were retrospectively included, with 169 classified as treatment success and 59 as failure. SR images were generated using a deep learning-based generative adversarial network (GAN). Radiomic features were extracted from both normal-resolution (NR) and SR ultrasound images. Features with intraclass correlation coefficient (ICC) ≥ 0.75 were retained after intra- and inter-observer evaluation. Feature selection involved statistical testing and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Random forest algorithms were used to construct NR and SR models. A clinical model based on serum β-hCG was also developed. The Clin-SR model was constructed by fusing SR radiomics with β-hCG values. Model performance was evaluated using area under the curve (AUC), calibration, and decision curve analysis (DCA). An independent temporal validation cohort (n = 40; 20 failures, 20 successes) was used to validation of the nomogram derived from the Clin-SR model. The SR model significantly outperformed the NR model in the test cohort (AUC: 0.791 ± 0.015 vs. 0.629 ± 0.083). In a representative iteration, the Clin-SR fusion model achieved an AUC of 0.870 ± 0.015, with good calibration and net clinical benefit, suggesting reliable performance in predicting conservative treatment failure. In the independent validation cohort, the nomogram demonstrated good generalizability with an AUC of 0.808 and consistent calibration across risk thresholds. Key contributing radiomic features included Gray Level Variance and Voxel Volume, reflecting lesion heterogeneity and size. The Clin-SR model, which integrates deep learning-enhanced SR ultrasound radiomics with serum β-hCG, offers a robust and non-invasive tool for predicting conservative treatment failure in ectopic pregnancy. This multimodal approach enhances early risk stratification and supports personalized clinical decision-making, potentially reducing overtreatment and emergency interventions.

Hepatocellular carcinoma (HCC) is a major global health concern, with microvascular invasion (MVI) being a critical prognostic factor linked to early recurrence and poor survival. Preoperative MVI prediction remains challenging, but recent advancements in dynamic contrast-enhanced ultrasound (CEUS) imaging combined with artificial intelligence show promise in improving prediction accuracy. CEUS offers real-time visualization of tumor vascularity, providing unique insights into MVI characteristics. This study proposes a novel deep relation reasoning approach to address the challenges of modeling intricate temporal relationships and extracting complex spatial features from CEUS video frames. Our method integrates CEUS video sequences and introduces a visual graph reasoning framework that correlates intratumoral and peritumoral features across various imaging phases. The system employs dual-path feature extraction, MVI pattern topology construction, Graph Convolutional Network learning, and an MVI pattern discovery module to capture complex features while providing interpretable results. Experimental findings demonstrate that our approach surpasses existing state-of-the-art models in accuracy, sensitivity, and specificity for MVI prediction. The system achieved superiors accuracy, sensitivity, specificity and AUC. These advancements promise to enhance HCC diagnosis and management, potentially revolutionizing patient care. The method's robust performance, even with limited data, underscores its potential for practical clinical application in improving the efficacy and efficiency of HCC patient diagnosis and treatment planning.

Carotid ultrasound image segmentation and classification are crucial in assessing the severity of carotid plaques which serve as a major cause of ischemic stroke. Although many methods are employed for carotid plaque segmentation and classification, treating these tasks separately neglects their interrelatedness. Currently, there is limited research exploring the key information of both plaque and background regions, and collecting and annotating extensive segmentation data is a costly and time-intensive task. To address these two issues, we propose an end-to-end semi-supervised multi-task learning network(SML-Net), which can classify plaques while performing segmentation. SML-Net identifies regions by extracting image features and fuses multi-scale features to improve semi-supervised segmentation. SML-Net effectively utilizes plaque and background regions from the segmentation results and extracts features from various dimensions, thereby facilitating the classification task. Our experimental results indicate that SML-Net achieves a plaque classification accuracy of 86.59% and a Dice Similarity Coefficient (DSC) of 82.36%. Compared to the leading single-task network, SML-Net improves DSC by 1.2% and accuracy by 1.84%. Similarly, when compared to the best-performing multi-task network, our method achieves a 1.05% increase in DSC and a 2.15% improvement in classification accuracy.

Accurate fetal growth evaluation is crucial for monitoring fetal health, with crown-rump length (CRL) being the gold standard for estimating gestational age and assessing growth during the first trimester. To enhance CRL evaluation accuracy and efficiency, we developed an artificial intelligence (AI)-based model (3DCRL-Net) using the 3D U-Net architecture for automatic landmark detection to achieve CRL plane localization and measurement in 3D ultrasound. We then compared its performance to that of experienced radiologists using both 2D and 3D ultrasound for fetal growth assessment. This prospective consecutive study collected fetal data from 1,326 ultrasound screenings conducted at 11-14 weeks of gestation (June 2021 to June 2023). Three experienced radiologists performed fetal screening using 2D video (2D-RAD) and 3D volume (3D-RAD) to obtain the CRL plane and measurement. The 3DCRL-Net model automatically outputs the landmark position, CRL plane localization and measurement. Three specialists audited the planes achieved by radiologists and 3DCRL-Net as standard or non-standard. The performance of CRL landmark detection, plane localization, measurement and time efficiency was evaluated in the internal testing dataset, comparing results with 3D-RAD. In the external dataset, CRL plane localization, measurement accuracy, and time efficiency were compared among the three groups. The internal dataset consisted of 126 cases in the testing set (training: validation: testing = 8:1:1), and the external dataset included 245 cases. On the internal testing set, 3DCRL-Net achieved a mean absolute distance error of 1.81 mm for the nine landmarks, higher accuracy in standard plane localization compared to 3D-RAD (91.27% vs. 80.16%), and strong consistency in CRL measurements (mean absolute error (MAE): 1.26 mm; mean difference: 0.37 mm, P = 0.70). The average time required per fetal case was 2.02 s for 3DCRL-Net versus 2 min for 3D-RAD (P < 0.001). On the external testing dataset, 3DCRL-Net demonstrated high performance in standard plane localization, achieving results comparable to 2D-RAD and 3D-RAD (accuracy: 91.43% vs. 93.06% vs. 86.12%), with strong consistency in CRL measurements, compared to 2D-RAD, which showed an MAE of 1.58 mm and a mean difference of 1.12 mm (P = 0.25). For 2D-RAD vs. 3DCRL-Net, the Pearson correlation and R² were 0.96 and 0.93, respectively, with an MAE of 0.11 ± 0.12 weeks. The average time required per fetal case was 5 s for 3DCRL-Net, compared to 2 min for 3D-RAD and 35 s for 2D-RAD (P < 0.001). The 3DCRL-Net model provides a rapid, accurate, and fully automated solution for CRL measurement in 3D ultrasound, achieving expert-level performance and significantly improving the efficiency and reliability of first-trimester fetal growth assessment.

Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of the fetus and image quality fluctuation, its interpretation is quite challenging. Although deep learning include Convolution Neural Networks (CNNs) have been promising, they have largely been limited to one task or the other, such as the segmentation or detection of fetal structures, thus lacking an integrated solution that accounts for the intricate interplay between anatomical structures. To overcome these limitations, Fetal-Net-a new deep learning architecture that integrates Multi-Scale-CNNs and transformer layers-was developed. The model was trained on a large, expertly annotated set of more than 12,000 ultrasound images across different anatomical planes for effective identification of fetal structures and anomaly detection. Fetal-Net achieved excellent performance in anomaly detection, with precision (96.5%), accuracy (97.5%), and recall (97.8%) showed robustness factor against various imaging settings, making it a potent means of augmenting prenatal care through refined ultrasound image interpretation.

Accurately assessing the risk stratification of cN0 papillary thyroid carcinoma (PTC) preoperatively aids in making treatment decisions. We integrated preoperative ultrasound and cytological images of patients to develop and validate a multimodal deep learning (DL) model for non-invasive assessment of N0 PTC risk stratification before surgery. In this retrospective multicenter group study, we developed a comprehensive DL model based on ultrasound and cytological images. The model was trained and validated on 890 PTC patients undergoing thyroidectomy and lymph node dissection across five medical centers. The testing group included 107 patients from one medical center. We analyzed the model's performance, including the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. The combined DL model demonstrated strong performance, with an area under the curve (AUC) of 0.922 (0.866-0.979) in the internal validation group and an AUC of 0.845 (0.794-0.895) in the testing group. The diagnostic performance of the combined DL model surpassed that of clinical models. Image region heatmaps assisted in interpreting the diagnosis of risk stratification. The multimodal DL model based on ultrasound and cytological images can accurately determine the risk stratification of N0 PTC and guide treatment decisions.

The increasing incidence of thyroid nodules (TN) raises concerns about overdiagnosis and overtreatment. This study evaluates the clinical and economic impact of KOIOS, an FDA-approved artificial intelligence (AI) tool for the management of TN. A retrospective analysis was conducted on 176 patients who underwent thyroid surgery between May 2022 and November 2024. Ultrasound images were evaluated independently by an expert and novice operators using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS), while KOIOS provided AI-adapted risk stratification. Sensitivity, specificity, and Receiver-Operating Curve (ROC) analysis were performed. The incremental cost-effectiveness ratio (ICER) was defined based on the number of optimal care interventions (FNAB and thyroid surgery). Both deterministic and probabilistic sensitivity analyses were conducted to evaluate model robustness. KOIOS AI demonstrated similar diagnostic performance to the expert operator (AUC: 0.794, 95% CI: 0.718-0.871 vs. 0.784, 95% CI: 0.706-0.861; p = 0.754) and significantly outperformed the novice operator (AUC: 0.619, 95% CI: 0.526-0.711; p < 0.001). ICER analysis estimated the cost per additional optimal care decision at -€8,085.56, indicating KOIOS as a dominant and cost-saving strategy when considering a third-party payer perspective over a one-year horizon. Deterministic sensitivity analysis identified surgical costs as the main drivers of variability, while probabilistic analysis consistently favored KOIOS as the optimal strategy. KOIOS AI is a cost-effective alternative, particularly in reducing overdiagnosis and overtreatment for benign TNs. Prospective, real-life studies are needed to validate these findings and explore long-term implications.