Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance of AI models is unclear. We aimed to develop machine learning models using shear wave elastography (SWE) data to identify malignant breast lesions and to test the models' generalizability by validating them on external data generated by both the original updated software versions. We developed and validated different machine learning models (GLM, MARS, XGBoost, SVM) using multicenter, international SWE data (NCT02638935) using tenfold cross-validation. Findings were compared to the histopathologic evaluation of the biopsy specimen or 2-year follow-up. The outcome measure was the area under the curve (AUROC). We included 1288 cases in the development set using the original imaging software and 385 cases in the validation set using both, original and updated software. In the external validation set, the GLM and XGBoost models showed better performance with the updated software data compared to the original software data (AUROC 0.941 vs. 0.902, p < 0.001 and 0.934 vs. 0.872, p < 0.001). The MARS model showed worse performance with the updated software data (0.847 vs. 0.894, p = 0.045). SVM was not calibrated. In this multicenter study using SWE data, some machine learning models demonstrated great potential to bridge the gap between original software and updated software, whereas others exhibited weak generalizability.

Accurate three-dimensional (3D) segmentation of hepatic vascular networks is crucial for supporting ultrasound-mediated theranostics for liver diseases. Despite advancements in deep learning techniques, accurate segmentation remains challenging due to ultrasound image quality issues, including intensity and contrast fluctuations. This study introduces intensity transformation-based data augmentation methods to improve deep convolutional neural network-based segmentation of hepatic vascular networks. We employed a 3D U-Net, which leverages spatial contextual information, as the baseline. To address intensity and contrast fluctuations and improve 3D U-Net performance, we implemented data augmentation using high-contrast intensity transformation with S-shaped tone curves and low-contrast intensity transformation with Gamma and inverse S-shaped tone curves. We conducted validation experiments on 78 ultrasound volumes to evaluate the effect of both geometric and intensity transformation-based data augmentations. We found that high-contrast intensity transformation-based data augmentation decreased segmentation accuracy, while low-contrast intensity transformation-based data augmentation significantly improved Recall and Dice. Additionally, combining geometric and low-contrast intensity transformation-based data augmentations, through an OR operation on their results, further enhanced segmentation accuracy, achieving improvements of 9.7% in Recall and 3.3% in Dice. This study demonstrated the effectiveness of low-contrast intensity transformation-based data augmentation in improving volumetric segmentation of hepatic vascular networks from ultrasound volumes.

With developments in computer science and technology, great progress has been made in three-dimensional (3D) ultrasound. Recently, ultrasound-based 3D bone modelling has attracted much attention, and its accuracy has been studied for the femur, tibia, and spine. The use of ultrasound allows data for bone surface to be acquired non-invasively and without radiation. Freehand 3D ultrasound of the bone surface can be roughly divided into two steps: segmentation of the bone surface from two-dimensional (2D) ultrasound images and 3D reconstruction of the bone surface using the segmented images. The aim of this study was to develop an automatic algorithm to segment the midface bone surface from 2D ultrasound images based on deep learning methods. Six deep learning networks were trained (nnU-Net, U-Net, ConvNeXt, Mask2Former, SegFormer, and DDRNet). The performance of the algorithms was compared with that of the ground truth and evaluated by Dice coefficient (DC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), average symmetric surface distance (ASSD), precision, recall, and time. nnU-Net yielded the highest DC of 89.3% ± 13.6% and the lowest ASSD of 0.11 ± 0.40 mm. This study showed that nnU-Net can automatically and effectively segment the midfacial bone surface from 2D ultrasound images.

Left ventricular ejection fraction (LVEF) is a critical indicator of cardiac function, aiding in the assessment of heart conditions. Accurate segmentation of the left ventricle (LV) is essential for LVEF calculation. However, current methods are often limited by small datasets and exhibit poor generalization. While leveraging large models can address this issue, many fail to capture multi-scale information and introduce additional burdens on users to generate prompts. To overcome these challenges, we propose LV-SAM, a model based on the large model SAM-Med2D, for accurate LV segmentation. It comprises three key components: an image encoder with a multi-scale adapter (MSAd), a multimodal prompt encoder (MPE), and a multi-scale decoder (MSD). The MSAd extracts multi-scale information at the encoder level and fine-tunes the model, while the MSD employs skip connections to effectively utilize multi-scale information at the decoder level. Additionally, we introduce an automated pipeline for generating self-extracted dense prompts and use a large language model to generate text prompts, reducing the user burden. The MPE processes these prompts, further enhancing model performance. Evaluations on the CAMUS dataset show that LV-SAM outperforms existing SOAT methods in LV segmentation, achieving the lowest MAE of 5.016 in LVEF estimation.

To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (NAC). Ultrasound images from patients with pathologically confirmed breast cancer who underwent neoadjuvant therapy at our institution between July 2021 and December 2023 were retrospectively reviewed. Initially, the region of interest was delineated and segmented into multiple habitat areas using local feature delineation and cluster analysis techniques. Subsequently, radiomics features were extracted from each habitat area to construct 3 machine learning models. Finally, the model's efficacy was assessed through operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and calibration curve evaluation. A total of 945 patients were enrolled, with 333 demonstrating a favorable response to NAC and 612 exhibiting an unfavorable response to NAC. Through the application of habitat analysis techniques, 3 distinct habitat regions within the tumor were identified. Subsequently, a predictive model was developed by incorporating 19 radiomics features, and all 3 machine learning models demonstrated excellent performance in predicting treatment outcomes. Notably, extreme gradient boosting (XGBoost) exhibited superior performance with an area under the curve (AUC) of 0.872 in the training cohort and 0.740 in the testing cohort. Additionally, DCA and calibration curves were employed for further evaluation. The habitat analysis technique effectively distinguishes distinct biological subregions of breast cancer, while the established radiomics machine learning model predicts NAC response by forecasting residual cancer burden (RCB) classification.

Accurate liver lesion segmentation in ultrasound is a challenging task due to high speckle noise, ambiguous lesion boundaries, and inhomogeneous intensity distribution inside the lesion regions. This work first collected and annotated a dataset for liver lesion segmentation in ultrasound. In this paper, we propose a novel convolutional neural network to learn dual self-attentive transformer features for boosting liver lesion segmentation by leveraging the complementary information among non-local features encoded at different layers of the transformer architecture. To do so, we devise a dual self-attention refinement (DSR) module to synergistically utilize self-attention and reverse self-attention mechanisms to extract complementary lesion characteristics between cascaded multi-layer feature maps, assisting the model to produce more accurate segmentation results. Moreover, we propose a False-Positive-Negative loss to enable our network to further suppress the non-liver-lesion noise at shallow transformer layers and enhance more target liver lesion details into CNN features at deep transformer layers. Experimental results show that our network outperforms state-of-the-art methods quantitatively and qualitatively.

Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was conducted on 91 patients who underwent ultrasonography examinations between January 2016 and December 2023 and were subsequently diagnosed with pleomorphic adenoma or Warthin's tumor based on postoperative pathological findings. A total of 526 ultrasonography images were collected for analysis. Convolutional neural network (CNN) models, including ResNet18, MobileNetV3Small, and InceptionV3, were trained and validated using these images for the differentiation of pleomorphic adenoma and Warthin's tumor. Performance evaluation metrics such as receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were utilized. Two ultrasound physicians, with varying levels of expertise, conducted independent evaluations of the ultrasound images. Subsequently, a comparative analysis was performed between the diagnostic outcomes of the ultrasound physicians and the results obtained from the best-performing model. Inter-rater agreement between routine ultrasonography interpretation by the two expert ultrasonographers and the automatic identification diagnosis of the best model in relation to pathological results was assessed using kappa tests. The deep learning models achieved favorable performance in differentiating pleomorphic adenoma from Warthin's tumor. The ResNet18, MobileNetV3Small, and InceptionV3 models exhibited diagnostic accuracies of 82.4% (AUC: 0.932), 87.0% (AUC: 0.946), and 77.8% (AUC: 0.811), respectively. Among these models, MobileNetV3Small demonstrated the highest performance. The experienced ultrasonographer achieved a diagnostic accuracy of 73.5%, with sensitivity, specificity, positive predictive value, and negative predictive value of 73.7%, 73.3%, 77.8%, and 68.8%, respectively. The less-experienced ultrasonographer achieved a diagnostic accuracy of 69.0%, with sensitivity, specificity, positive predictive value, and negative predictive value of 66.7%, 71.4%, 71.4%, and 66.7%, respectively. The kappa test revealed strong consistency between the best-performing deep learning model and postoperative pathological diagnoses (kappa value: .778, <i>p</i>-value < .001). In contrast, the less-experienced ultrasonographer demonstrated poor consistency in image interpretations (kappa value: .380, <i>p</i>-value < .05). The diagnostic accuracy of the best deep learning model was significantly higher than that of the ultrasonographers, and the experienced ultrasonographer exhibited higher diagnostic accuracy than the less-experienced one. This study demonstrates the promising performance of a deep learning-based method utilizing ultrasonography images for the differentiation of pleomorphic adenoma and Warthin's tumor. The approach reduces subjective errors, provides decision support for clinicians, and improves diagnostic consistency.

This study aims to develop and validate an artificial intelligence (AI) model based on ultrasound (US) videos and images to improve the performance of junior sonologists in detecting deep infiltrating endometriosis (DE). In this retrospective study, data were collected from female patients who underwent US examinations and had DE. The US image records were divided into two parts. First, during the model development phase, an AI-DE model was trained employing YOLOv8 to detect pelvic DE nodules. Subsequently, its clinical applicability was evaluated by comparing the diagnostic performance of junior sonologists with and without AI-model assistance. The AI-DE model was trained using 248 images, which demonstrated high performance, with a mAP50 (mean Average Precision at IoU threshold 0.5) of 0.9779 on the test set. Total 147 images were used for evaluate the diagnostic performance. The diagnostic performance of junior sonologists improved with the assistance of the AI-DE model. The area under the receiver operating characteristic (AUROC) curve improved from 0.748 (95% CI, 0.624-0.867) to 0.878 (95% CI, 0.792-0.964; p < 0.0001) for junior sonologist A, and from 0.713 (95% CI, 0.592-0.835) to 0.798 (95% CI, 0.677-0.919; p < 0.0001) for junior sonologist B. Notably, the sensitivity of both sonologists increased significantly, with the largest increase from 77.42% to 94.35%. The AI-DE model based on US images showed good performance in DE detection and significantly improved the diagnostic performance of junior sonologists.

Breast cancer poses a major health concern for women globally. Effective segmentation of breast tumors for ultrasound images is crucial for early diagnosis and treatment. Conventional convolutional neural networks have shown promising results in this domain but face challenges due to image complexities and are computationally expensive, limiting their practical application in real-time clinical settings. We propose Eff-AResUNet-GL, a segmentation model using EfficienetNet-B3 as the encoder. This design integrates attention gates in skip connections to focus on significant features and residual blocks in the decoder to retain details and reduce gradient loss. Additionally, guided loss functions are applied at each decoder layer to generate better features, subsequently improving segmentation accuracy. Experimental results on BUSIS and Dataset B demonstrate that Eff-AResUNet-GL achieves superior performance and computational efficiency compared to state-of-the-art models, showing robustness in handling complex breast ultrasound images. Eff-AResUNet-GL offers a practical, high-performing solution for breast tumor segmentation, demonstrating potential clinical through improved segmentation accuracy and efficiency.

This study aims to develop and validate an ultrasoundbased habitat imaging and peritumoral radiomics model for predicting high-risk capsule characteristics for recurrence of pleomorphic adenoma (PA) of the parotid gland while also exploring the optimal range of peritumoral region. Retrospective analysis was conducted on 325 patients (171 in training set, 74 in validation set and 80 in testing set) diagnosed with PA at two medical centers. Univariate and multivariate logistic regression analyses were performed to identify clinical risk factors. The tumor was segmented into four habitat subregions using K-means clustering, with peri-tumor regions expanded at thicknesses of 1/3/5mm. Radiomics features were extracted from intra-tumor, habitat subregions, and peritumoral regions respectively to construct predictive models, integrating three machine learning classifiers: SVM, RandomForest, and XGBoost. Additionally, a combined model was developed by incorporating peritumoral features and clinical factors based on habitat imaging. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). SHAP analysis was employed to improve the interpretability. The RandomForest model in habitat imaging consistently outperformed other models in predictive performance, with AUC values of 0.881, 0.823, and 0.823 for the training set, validation set, and testing set respectively. Incorporating peri-1mm features and clinical factors into the combined model slightly improved its performance, resulting in AUC values of 0.898, 0.833, and 0.829 for each set. The calibration curves and DCA exhibited excellent fit for the combined model while providing great clinical net benefit. The combined model exhibits robust predictive performance in identifying high-risk capsule characteristics for recurrence of PA in the parotid gland. This model may assist in determining optimal surgical margin and assessing patients' prognosis.