Only in recent years it has been demonstrated that the thoracolumbar fascia is involved in low back pain (LBP), thus highlighting its implications for treatments. Furthermore, an easily accessible and non-invasive way to investigate the fascia in real time is the ultrasound examination, which to be reliable as is, it must overcome the challenges related to the configuration of the machine and the experience of the operator. Therefore, the lack of a clear understanding of the fascial system combined with the penalty related to the setting of the ultrasound acquisition has generated a gap that makes its effective evaluation difficult during clinical routine. The aim of the present work is to fill this gap by investigating the effectiveness of using a deep learning approach to segment the thoracolumbar fascia from ultrasound imaging. A total of 538 ultrasound images of the thoracolumbar fascia of LBP subjects were finally used to train and test a deep learning network. An additional test set (so-called Test set 2) was collected from another center, operator, machine manufacturer, patient cohort, and protocol to improve the generalizability of the study. A U-Net-based architecture was demonstrated to be able to segment these structures with a final training accuracy of 0.99 and a validation accuracy of 0.91. The accuracy of the prediction computed on a test set (87 images not included in the training set) reached the 0.94, with a mean intersection over union index of 0.82 and a Dice-score of 0.76. These latter metrics were outperformed by those in Test set 2. The validity of the predictions was also verified and confirmed by two expert clinicians. Automatic identification of the thoracolumbar fascia has shown promising results to thoroughly investigate its alteration and target a personalized rehabilitation intervention based on each patient-specific scenario.

Ultrasound localization microscopy (ULM) has revolutionized microvascular imaging by breaking the acoustic diffraction limit. However, different ULM workflows depend heavily on distinct prior knowledge, such as the impulse response and empirical selection of parameters (e.g., the number of microbubbles (MBs) per frame M), or the consistency of training-test dataset in deep learning (DL)-based studies. We hereby propose a general ULM pipeline that reduces priors. Our approach leverages a DL model that simultaneously distills microbubble signals and reduces speckle from every frame without estimating the impulse response and M. Our method features an efficient channel attention vision transformer (ViT) and a progressive learning strategy, enabling it to learn global information through training on progressively increasing patch sizes. Ample synthetic data were generated using the k-Wave toolbox to simulate various MB patterns, thus overcoming the deficiency of labeled data. The ViT output was further processed by a standard radial symmetry method for sub-pixel localization. Our method performed well on model-unseen public datasets: one in silico dataset with ground truth and four in vivo datasets of mouse tumor, rat brain, rat brain bolus, and rat kidney. Our pipeline outperformed conventional ULM, achieving higher positive predictive values (precision in DL, 0.88-0.41 vs. 0.83-0.16) and improved accuracy (root-mean-square errors: 0.25-0.14 λ vs. 0.31-0.13 λ) across a range of signal-to-noise ratios from 60 dB to 10 dB. Our model could detect more vessels in diverse in vivo datasets while achieving comparable resolutions to the standard method. The proposed ViT-based model, seamlessly integrated with state-of-the-art downstream ULM steps, improved the overall ULM performance with no priors.

As ultrasound (US) is the most accurate tool for assessing the thyroid nodule (TN) risk of malignancy (RoM), international societies have published various Thyroid Imaging and Reporting Data Systems (TIRADSs). With the recent advent of artificial intelligence (AI), clinicians and researchers should ask themselves how AI could interpret the terminology of the TIRADSs and whether or not AIs agree in the risk assessment of TNs. The study aim was to analyze the interobserver agreement (IOA) between AIs in assessing the RoM of TNs across various TIRADSs categories using a cases series created combining TIRADSs descriptors. ChatGPT, Google Gemini, and Claude were compared. ACR-TIRADS, EU-TIRADS, and K-TIRADS, were employed to evaluate the AI assessment. Multiple written scenarios for the three TIRADS were created, the cases were evaluated by the three AIs, and their assessments were analyzed and compared. The IOA was estimated by comparing the kappa (κ) values. Ninety scenarios were created. With ACR-TIRADS the IOA analysis gave κ = 0.58 between ChatGPT and Gemini, 0.53 between ChatGPT and Claude, and 0.90 between Gemini and Claude. With EU-TIRADS it was observed κ value = 0.73 between ChatGPT and Gemini, 0.62 between ChatGPT and Claude, and 0.72 between Gemini and Claude. With K-TIRADS it was found κ = 0.88 between ChatGPT and Gemini, 0.70 between ChatGPT and Claude, and 0.61 between Gemini and Claude. This study found that there were non-negligible variability between the three AIs. Clinicians and patients should be aware of these new findings.

PCOS (Poly-Cystic Ovary Syndrome) is a multifaceted disorder that often affects the ovarian morphology of women of their reproductive age, resulting in the development of numerous cysts on the ovaries. Ultrasound imaging typically diagnoses PCOS, which helps clinicians assess the size, shape, and existence of cysts in the ovaries. Nevertheless, manual ultrasound image analysis is often challenging and time-consuming, resulting in inter-observer variability. To effectively treat PCOS and prevent its long-term effects, prompt and accurate diagnosis is crucial. In such cases, a prediction model based on deep learning can help physicians by streamlining the diagnosis procedure, reducing time and potential errors. This article proposes a novel integrated approach, QEI-SAM (Quality Enhanced Image - Segment Anything Model), for enhancing image quality and ovarian cyst segmentation for accurate prediction. GAN (Generative Adversarial Networks) and CNN (Convolutional Neural Networks) are the most recent cutting-edge innovations that have supported the system in attaining the expected result. The proposed QEI-SAM model used Enhanced Super Resolution Generative Adversarial Networks (ESRGAN) for image enhancement to increase the resolution, sharpening the edges and restoring the finer structure of the ultrasound ovary images and achieved a better SSIM of 0.938, PSNR value of 38.60 and LPIPS value of 0.0859. Then, it incorporates the Segment Anything Model (SAM) to segment ovarian cysts and achieve the highest Dice coefficient of 0.9501 and IoU score of 0.9050. Furthermore, Convolutional Neural Network - ResNet 50, ResNet 101, VGG 16, VGG 19, AlexNet and Inception v3 have been implemented to diagnose PCOS promptly. Finally, VGG 19 has achieved the highest accuracy of 99.31%.

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Crossview Local- and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-of-the-art infection detection techniques.

Segmentation is one of the most significant steps in image processing. Segmenting an image is a technique that makes it possible to separate a digital image into various areas based on the different characteristics of pixels in the image. In particular, the segmentation of breast ultrasound images is widely used for cancer identification. As a result of image segmentation, it is possible to make early diagnoses of a diseases via medical images in a very effective way. Due to various ultrasound artifacts and noises, including speckle noise, low signal-to-noise ratio, and intensity heterogeneity, the process of accurately segmenting medical images, such as ultrasound images, is still a challenging task. In this paper, we present a new method to improve the accuracy and effectiveness of breast ultrasound image segmentation. More precisely, we propose a neural network (NN) based on U-Net and an encoder-decoder architecture. By taking U-Net as the basis, both the encoder and decoder parts are developed by combining U-Net with other deep neural networks (Res-Net and MultiResUNet) and introducing a new approach and block (Co-Block), which preserve as much as possible the low-level and the high-level features. The designed network is evaluated using the Breast Ultrasound Images (BUSI) Dataset. It consists of 780 images, and the images are categorized into three classes, which are normal, benign, and malignant. According to our extensive evaluations on a public breast ultrasound dataset, the designed network segments the breast lesions more accurately than other state-of-the-art deep learning methods. With only 8.88 M parameters, our network (CResU-Net) obtained 82.88%, 77.5%, 90.3%, and 98.4% in terms of Dice similarity coefficients (DSC), intersection over union (IoU), area under curve (AUC), and global accuracy (ACC), respectively, on the BUSI dataset.

BackgroundAccurate left ventricular outflow tract obstruction (LVOTO) assessment is crucial for hypertrophic cardiomyopathy (HCM) management and prognosis. Traditional methods, requiring multiple views, Doppler, and provocation, is often infeasible, especially where resources are limited. This study aimed to develop and validate a deep learning (DL) model capable of predicting severe LVOTO in HCM patients using only the parasternal long-axis (PLAX) view from transthoracic echocardiography (TTE). MethodsA DL model was trained on PLAX videos extracted from TTE examinations (developmental dataset, n=1,007) to capture both morphological and dynamic motion features, generating a DL index for LVOTO (DLi-LVOTO, range 0-100). Performance was evaluated in an internal test dataset (ITDS, n=87) and externally validated in the distinct hospital dataset (DHDS, n=1,334) and the LVOTO reduction treatment dataset (n=156). ResultsThe model achieved high accuracy in detecting severe LVOTO (pressure gradient[&ge;] 50mmHg), with area under the receiver operating characteristics curve (AUROC) of 0.97 (95% confidence interval: 0.92-1.00) in ITDS and 0.93 (0.92-0.95) in DHDS. At a DLi-LVOTO threshold of 70, the model demonstrated a specificity of 97.3% and negative predictive value (NPV) of 96.1% in ITDS. In DHDS, a cutoff of 60 yielded a specificity of 94.6% and NPV of 95.5%. DLi-LVOTO also decreased significantly after surgical myectomy or Mavacamten treatment, correlating with reductions in peak pressure gradient (p<0.001 for all). ConclusionsOur DL-based approach predicts severe LVOTO using only the PLAX view from TTE, serving as a complementary tool, particularly in resource-limited settings or when Doppler is unavailable, and for monitoring treatment response.

Automated Whole-Breast Ultrasound (ABUS) has been widely used as an important tool in breast cancer diagnosis due to the ability of this technique to provide complete three-dimensional (3D) images of breasts. To eliminate the risk of misdiagnosis, computer-aided diagnosis (CADx) systems have been proposed to assist radiologists. Convolutional neural networks (CNNs), renowned for the automatic feature extraction capabilities, have developed rapidly in medical image analysis, and this study proposes a CADx system based on 3D CNN for ABUS. This study used a private dataset collected at Sun Yat-Sen University Cancer Center (SYSUCC) from 396 breast tumor patients. First, the tumor volume of interest (VOI) was extracted and resized, and then the tumor was enhanced by histogram equalization. Second, a 3D U-Net++ was employed to segment the tumor mask. Finally, the VOI, the enhanced VOI, and the corresponding tumor mask were fed into a 3D Attention-Inception network to classify the tumor as benign or malignant. The experiment results indicate an accuracy of 89.4%, a sensitivity of 91.2%, a specificity of 87.6%, and an area under the receiver operating characteristic curve (AUC) of 0.9262, which suggests that the proposed CADx system for ABUS images rivals the performance of experienced radiologists in tumor diagnosis tasks. This study proposes a CADx system consisting of a 3D U-Net++ tumor segmentation model and a 3D attention inception neural network tumor classification model for diagnosis in ABUS images. The results indicate that the proposed CADx system is effective and efficient in tumor diagnosis tasks.

Urodynamic studies (UDS) are essential for evaluating lower urinary tract function but are limited by patient discomfort, lack of standardization and diagnostic variability. Advances in technology aim to address these challenges and improve diagnostic accuracy and patient comfort. AUM offers physiological assessment by allowing natural bladder filling and monitoring during daily activities. Compared to conventional UDS, AUM demonstrates higher sensitivity for detecting detrusor overactivity and underlying pathophysiology. However, it faces challenges like motion artifacts, catheter-related discomfort, and difficulty measuring continuous bladder volume. Emerging devices such as Urodynamics Monitor and UroSound offer more patient-friendly alternatives. These tools have the potential to improve diagnostic accuracy for bladder pressure and voiding metrics but remain limited and still require further validation and testing. Ultrasound-based modalities, including dynamic ultrasonography and shear wave elastography, provide real-time, noninvasive assessment of bladder structure and function. These modalities are promising but will require further development of standardized protocols. AI and machine learning models enhance diagnostic accuracy and reduce variability in UDS interpretation. Applications include detecting detrusor overactivity and distinguishing bladder outlet obstruction from detrusor underactivity. However, further validation is required for clinical adoption. Advances in AUM, wearable technologies, ultrasonography, and AI demonstrate potential for transforming UDS into a more accurate, patient-centered tool. Despite significant progress, challenges like technical complexity, standardization, and cost-effectiveness must be addressed to integrate these innovations into routine practice. Nonetheless, these technologies provide the possibility of a future of improved diagnosis and treatment of lower urinary tract dysfunction.

Acute kidney injury (AKI) post-renal transplantation often has a poor prognosis. This study aimed to identify patients with elevated risks of AKI after kidney transplantation. A retrospective analysis was conducted on 422 patients who underwent kidney transplants from January 2020 to April 2023. Participants from 2020 to 2022 were randomized to training group (n=261) and validation group 1 (n=113), and those in 2023, as validation group 2 (n=48). Risk factors were determined by employing logistic regression analysis alongside the least absolute shrinkage and selection operator, making use of ultrasound hemodynamic, clinical, and laboratory information. Models for prediction were developed using logistic regression analysis and six machine-learning techniques. The evaluation of the logistic regression model encompassed its discrimination, calibration, and applicability in clinical settings, and a nomogram was created to illustrate the model. SHapley Additive exPlanations were used to explain and visualize the best of the six machine learning models. The least absolute shrinkage and selection operator combined with logistic regression identified and incorporated five risk factors into the predictive model. The logistic regression model (AUC=0.927 in the validation set 1; AUC=0.968 in the validation set 2) and the random forest model (AUC=0.946 in the validation set 1;AUC=0.996 in the validation set 2) showed good performance post-validation, with no significant difference in their predictive accuracy. These findings can assist clinicians in the early identification of patients at high risk for AKI, allowing for timely interventions and potentially enhancing the prognosis following kidney transplantation.