Journal of Medical Ultrasonics最新文献

Purpose: In recent years, computer-aided diagnosis (CAD) using deep learning methods for medical images has been studied. Although studies have been conducted to classify ultrasound images of tumors of the liver into four categories (liver cysts (Cyst), liver hemangiomas (Hemangioma), hepatocellular carcinoma (HCC), and metastatic liver cancer (Meta)), no studies with additional information for deep learning have been reported. Therefore, we attempted to improve the classification accuracy of ultrasound images of hepatic tumors by adding hepatitis virus infection information to deep learning.

Methods: Four combinations of hepatitis virus infection information were assigned to each image, plus or minus HBs antigen and plus or minus HCV antibody, and the classification accuracy was compared before and after the information was input and weighted to fully connected layers.

Results: With the addition of hepatitis virus infection information, accuracy changed from 0.574 to 0.643. The F1-Score for Cyst, Hemangioma, HCC, and Meta changed from 0.87 to 0.88, 0.55 to 0.57, 0.46 to 0.59, and 0.54 to 0.62, respectively, remaining the same for Hemangioma but increasing for the rest.

Conclusion: Learning hepatitis virus infection information showed the highest increase in the F1-Score for HCC, resulting in improved classification accuracy of ultrasound images of hepatic tumors.

Purpose: This study aimed to evaluate an innovative anterolateral approach using portable ultrasound, comparing the accuracy and safety of portable ultrasound-guided, conventional ultrasound-guided, and landmark-based blind injection techniques.

Methods: In this single-centre, prospective, randomised controlled trial, 117 patients with hip pain were randomly assigned to three groups: landmark-based blind injection (n = 39), conventional ultrasound-guided injection (n = 39), and portable ultrasound-guided injection (n = 39). Each patient received a unilateral injection of 2.5 ml hyaluronic acid and 1 ml betamethasone via the anterolateral approach, using parameters optimized from previous research. Primary endpoints included success and accuracy rates, while secondary endpoints comprised post-injection visual analogue scale (VAS) pain scores, procedure time, puncture depth, and complications.

Results: The portable ultrasound group achieved 100% success and accuracy rates, comparable to the conventional ultrasound group, whereas the blind group showed lower success (87.2%) and accuracy (79.4%) rates. Post-injection VAS pain scores were significantly lower in the portable ultrasound group (1.95, SD 0.99) compared with the blind group (2.95, SD 1.61; p = 0.007) and similar to those in the conventional ultrasound group (2.41, SD 1.27; p = 0.337). Procedure times were comparable across all groups, and no significant differences in puncture depth were observed. Importantly, no injection-related complications were reported.

Conclusion: Portable ultrasound-guided injections via the anterolateral approach demonstrate accuracy and safety comparable to conventional ultrasound-guided injections. Additionally, the portable device offers advantages in portability, reduced space requirements, and cost-effectiveness, thereby enhancing clinical utility in outpatient settings.

Purpose: Carotid plaque is a major risk factor for cerebral infarction. Ultrasonography (US) is extensively used for screening carotid plaque, but US images contain more noise than those of computed tomography and magnetic resonance imaging, and the edges of the plaque regions are unclear. In addition, B-mode echogenicity evaluation, which is important for plaque risk assessment, has challenges involving the subjectivity of the evaluator. Although previous studies on carotid plaque assessment have included plaque segmentation, most studies involved manual operations. In this study, we propose an automated scheme of plaque classification based on segmentation in carotid US images using the transformer approach, to resolve the issues of previous studies and to perform plaque echogenicity classification.

Methods: The B-mode video captured in the long-axis cross-section was converted to still images, and region extraction and echogenicity classification were performed using TransUNet. The results of the TransUNet output and US images were fed into the Vision Transformer (ViT) for classification into hypoechoic or isoechoic-hyperechoic plaques.

Results: The Dice index, which indicates the accuracy of plaque region extraction, was 0.592. The Dice indices by echogenicity were 0.200, 0.493, and 0.542 for the hypoechoic, isoechoic, and hyperechoic regions, respectively. The balanced accuracy, which indicates the classification accuracy, was 79.6%. The correct classification rate for high-risk hypoechoic plaques was 95.2%.

Conclusion: These results suggest that the proposed method is useful for evaluating the echogenicity classification of carotid artery plaques.