Journal of Ultrasound in Medicine最新文献

{"title":"ULTRA-Metrics: Delphi-Derived Framework for Assessing Ultrasound Competency.","authors":"Steve Reid, Alberto Goffi, Ean Tsou, Emanuele Pivetta, Suean Pascoe, Jessica Solis-McCarthy, Mark Foster, Chris Gelabert, Mike Smith, Colin Bell, Erica Clarke Whalen, Hannah Latta, Janeve Desy, Simon Hayward, Hayley Israel, Andrew Leamon, Marcus Peck, Adrian Wong, Tanping Wong, Chris Yap, Emma M L Chung","doi":"10.1002/jum.70074","DOIUrl":"https://doi.org/10.1002/jum.70074","url":null,"abstract":"<p><strong>Objectives: </strong>Ultrasound competency is critical in modern healthcare, yet no standardized framework currently supports ultrasound skill monitoring across diverse clinical settings and user types. Existing frameworks often lack generalizability, overemphasize exam counts, and fail to assess key skills such as interpretation, limiting ultrasound's safe and effective integration into clinical practice. The objective of this study is to develop a consensus-based, universal framework for monitoring ultrasound competency across clinical applications and disciplines.</p><p><strong>Methods: </strong>A modified Delphi process was conducted with an international panel of Point-of-Care ultrasound experts. Panelists independently evaluated framework elements categorized by competency domains (experience, skills, autonomy), skill domains (indication, acquisition, interpretation, clinical integration), metrics (eg, exam counts, entrustability, interpretation accuracy, etc.), answer sets (score-based inputs used by assessors), and score criteria (requirements for each score). Consensus thresholds were defined as strong consensus at >84%, and weak consensus at 68-84%. Two Delphi rounds were completed.</p><p><strong>Results: </strong>Nineteen experts participated across 2 Delphi rounds. Strong consensus was reached to include 3 competency domains (experience, skills, autonomy) and 4 skill domains (indication, acquisition, interpretation, and clinical integration). Optional components, including the use of acquisition skill trees and varied answer set granularity, were favored by some participants to allow ultrasound programs to tailor the framework to specific examinations, assessment scenarios, and job roles.</p><p><strong>Conclusion: </strong>The resulting modular framework provides a flexible, consensus-based approach to ultrasound competency assessment, enabling cross-program comparisons and evaluation of training methods. Validation across diverse settings is needed to support its use in global competency standards and ultrasound education expansion.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carotid Bulb Geometry and Early Atherosclerosis: Need for Hemodynamic Validation.","authors":"Muhammet Cihat Çelik, Mücahit Aker, Macit Kalçık","doi":"10.1002/jum.70088","DOIUrl":"https://doi.org/10.1002/jum.70088","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cervical Maturity Assessment Based on Ultrasound Elastography E-Cervix: Cluster Analysis.","authors":"Ekaterina V Kopteeva, Vladislava V Khalenko, Olga V Pachuliia, Olesya N Bespalova","doi":"10.1002/jum.70078","DOIUrl":"https://doi.org/10.1002/jum.70078","url":null,"abstract":"<p><strong>Objective: </strong>To identify distinct cervical remodeling phenotypes based on ultrasound and quantitative elastography parameters using cluster analysis.</p><p><strong>Methods: </strong>This single-center, prospective, observational study was conducted from February 2023 to January 2024 and included 373 pregnant women with singleton pregnancies between 6 + 0 and 41 + 0 weeks of gestation (total number of ultrasound examinations: 516). Cervical elastography was performed using the E-Cervix software (Samsung Medison, W10 system), with assessment of quantitative parameters (hardness ratio [HR], elasticity contrast index [ECI], internal os [IOS], external os [EOS]).</p><p><strong>Results: </strong>Correlation analysis demonstrated that cervical hardness (HR) decreased, while tissue heterogeneity (ECI) and internal and external os deformation (IOS, EOS) increased with gestational age and cervical shortening. Cluster analysis identified three cervical phenotypes: the \"immature cervix\" phenotype had the greatest cervical length (37.4 mm [interquartile range 36.3-38.4]) and highest stiffness (HR 76.7% [75.7-77.7]), while the \"mature cervix\" phenotype showed the shortest length (24.1 mm [22.9-25.4]) and lowest stiffness (HR 38.1% [36.9-39.3]). Bishop scores differed significantly among the clusters (4, 6, and 8, respectively). The risk of spontaneous labor within 14 days was highest in the mature cervix group (HR 10.1, P < .001).</p><p><strong>Conclusion: </strong>Decreasing HR and increasing ECI, IOS, and EOS indicate progressive cervical softening as pregnancy advances and the cervix shortens. The combination of elastography parameters and cervical length can be used to objectively assess cervical remodeling during pregnancy.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based CAD System for Enhanced Breast Lesion Classification and Grading Using RFTSDP Approach.","authors":"Elaheh Norouzi Ghehi, Ali Fallah, Saeid Rashidi, Maryam Mehdizadeh Dastjerdi","doi":"10.1002/jum.70056","DOIUrl":"https://doi.org/10.1002/jum.70056","url":null,"abstract":"<p><strong>Objectives: </strong>Accurate detection of breast lesion type is crucial for optimizing treatment; however, due to the limited precision of current diagnostic methods, biopsies are often required. To address this limitation, we proposed radio frequency time series dynamic processing (RFTSDP) in 2020, which analyzes the dynamic response of tissue and the impact of scatterer displacement on RF echoes during controlled stimulations to enhance diagnostic information.</p><p><strong>Methods: </strong>We developed a vibration-generating device and collected ultrafast ultrasound data from 11 ex vivo breast tissue samples under different stimulations. Deep learning (DL) was used for automated feature extraction and lesion classification into 2, 3, and 5 categories. The performance of the convolutional neural network (CNN)-based RFTSDP method was compared with traditional machine learning techniques, which involved spectral and nonlinear feature extraction from RF time series, followed by a support vector machine (SVM).</p><p><strong>Results: </strong>With 65 Hz vibration, the DL-based RFTSDP method achieved 99.53 ± 0.47% accuracy in classifying and grading breast lesions. CNN consistently outperformed SVM, particularly under vibratory stimulation. In 5-class classification, CNN reached 98.01% versus 95.64% for SVM, with the difference being statistically significant (P < .05). Furthermore, the CNN-based RFTSDP method showed a 28.67% improvement in classification accuracy compared to the non-stimulation condition and the analysis of focused raw data.</p><p><strong>Conclusions: </strong>We developed a DL-based CAD system capable of classifying and grading breast lesions. This study demonstrates that the proposed system not only enhances classification but also ensures increased stability and robustness compared to traditional methods.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145206831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemorrhagic Ovarian Cysts over 50 mm: Value of Ultrasound Specialist Assessment.","authors":"Alan Zhu, Mark Sugi, Scott W Young, Tara A Morgan, Maitray D Patel","doi":"10.1002/jum.70081","DOIUrl":"https://doi.org/10.1002/jum.70081","url":null,"abstract":"<p><strong>Objectives: </strong>Evaluate the value of ultrasound specialist assessment of ovarian masses over 50 mm with sonograms interpreted as possible hemorrhagic ovarian cysts (HOC).</p><p><strong>Methods: </strong>Retrospective review of consecutive endovaginal pelvic US reports of premenopausal women at one organization identified adnexal masses over 50 mm maximum diameter designated as possibly being HOC (large HOC). Four ultrasound specialists reviewed studies, scoring two assessments as true or false: 1) US features are most consistent with either an ovarian hemorrhagic cyst (HOC) or endometrioma; 2) If assessment 1 was true, the mass meets O-RADS criteria of classic HOC except for size.</p><p><strong>Results: </strong>A total of 457 of 51,305 women (0.9%) had reports indicating possible large HOC. For 366 patients with established outcomes, 225 (61.5%) had a large HOC (87.1% ≤ 70 mm). Assessment 1 was true for 318 patients; outcomes showed 224 (70.4%) HOC, 89 (28.0%) endometriomas, 4 (1.3%) TOAs, and 1 (0.3%) cystadenoma. Assessment 1 was false for 48 patients; outcomes showed 1 (2.1%) HOC, 1 (2.1%) endometrioma, 6 (12.5%) other non-neoplastic cysts, 33 (68.8%) benign neoplasms, 5 (10.4%) borderline tumors, and 2 (4.2%) high-grade malignancies. The decrease in neoplasms and malignancies with true assessment 1 was significant (p < .001). Assessment 2 was true for 190 patients (180 [94.7%] HOC, 9 [4.7%] endometrioma, 1 [0.5%] cystadenoma), false for 128 patients (44 [34.4%] HOC, 80 [62.5%] endometrioma, and 4 [3.1%] TOA) (p < .001), and not applicable for 48 patients.</p><p><strong>Conclusions: </strong>Ultrasound specialist review of adnexal masses designated as a possible large HOC adds value since over 10% are neoplasms (nearly 2% malignant). Ultrasound specialist assessment can expedite additional imaging or intervention when necessary and obviate follow-up when HOC features are typical.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers: A Multicenter Study.","authors":"Ximena Wortsman, Manuel Lozano, Francisco Javier Rodriguez, Yessenia Valderrama, Gabriela Ortiz-Orellana, Luciana Zattar, Francisco de Cabo, Eliza Ducati, Rosa Sigrist, Claudia Fontan, Juliana Rezende, Claudia Gonzalez, Leonie Schelke, Julia Zavariz, Patricia Barrera, Peter Velthuis","doi":"10.1002/jum.70079","DOIUrl":"https://doi.org/10.1002/jum.70079","url":null,"abstract":"<p><strong>Objectives: </strong>Despite the growing use of artificial intelligence (AI) in medicine, imaging, and dermatology, to date, there is no information on the use of AI for discriminating cosmetic fillers on ultrasound (US).</p><p><strong>Methods: </strong>An international collaborative group working in dermatologic and esthetic US was formed and worked with the staff of the Department of Computer Science and AI of the Universidad de Granada to gather and process a relevant number of anonymized images. AI techniques based on deep learning (DL) with YOLO (you only look once) architecture, together with a bounding box annotation tool, allowed experts to manually delineate regions of interest for the discrimination of common cosmetic fillers under real-world conditions.</p><p><strong>Results: </strong>A total of 14 physicians from 6 countries participated in the AI study and compiled a final dataset comprising 1432 US images, including HA (hyaluronic acid), PMMA (polymethylmethacrylate), CaHA (calcium hydroxyapatite), and SO (silicone oil) filler cases. The model exhibits robust and consistent classification performance, with an average accuracy of 0.92 ± 0.04 across the cross-validation folds. YOLOv11 demonstrated outstanding performance in the detection of HA and SO, yielding F1 scores of 0.96 ± 0.02 and 0.94 ± 0.04, respectively. On the other hand, CaHA and PMMA show somewhat lower and less consistent performance in terms of precision and recall, with F1-scores around 0.83.</p><p><strong>Conclusions: </strong>AI using YOLOv11 allowed us to discriminate reliably between HA and SO using different complexity high-frequency US devices and operators. Further AI DL-specific work is needed to identify CaHA and PMMA more accurately.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145192005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a High-Performance Ultrasound Prediction Model for the Diagnosis of Endometrial Cancer: An Interpretable XGBoost Algorithm Utilizing SHAP Analysis.","authors":"Hongwei Lai, Qiumei Wu, Zongjie Weng, Guorong Lyu, Wenmin Yang, Fengying Ye","doi":"10.1002/jum.70082","DOIUrl":"https://doi.org/10.1002/jum.70082","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate an ultrasonography-based machine learning (ML) model for predicting malignant endometrial and cavitary lesions.</p><p><strong>Methods: </strong>This retrospective study was conducted on patients with pathologically confirmed results following transvaginal or transrectal ultrasound from 2021 to 2023. Endometrial ultrasound features were characterized using the International Endometrial Tumor Analysis (IETA) terminology. The dataset was ranomly divided (7:3) into training and validation sets. LASSO (least absolute shrinkage and selection operator) regression was applied for feature selection, and an extreme gradient boosting (XGBoost) model was developed. Performance was assessed via receiver operating characteristic (ROC) analysis, calibration, decision curve analysis, sensitivity, specificity, and accuracy.</p><p><strong>Results: </strong>Among 1080 patients, 6 had a non-measurable endometrium. Of the remaining 1074 cases, 641 were premenopausal and 433 postmenopausal. Performance of the XGBoost model on the test set: The area under the curve (AUC) for the premenopausal group was 0.845 (0.781-0.909), with a relatively low sensitivity (0.588, 0.442-0.722) and a relatively high specificity (0.923, 0.863-0.959); the AUC for the postmenopausal group was 0.968 (0.944-0.992), with both sensitivity (0.895, 0.778-0.956) and specificity (0.931, 0.839-0.974) being relatively high. SHapley Additive exPlanations (SHAP) analysis identified key predictors: endometrial-myometrial junction, endometrial thickness, endometrial echogenicity, color Doppler flow score, and vascular pattern in premenopausal women; endometrial thickness, endometrial-myometrial junction, endometrial echogenicity, and color Doppler flow score in postmenopausal women.</p><p><strong>Conclusion: </strong>The XGBoost-based model exhibited excellent predictive performance, particularly in postmenopausal patients. SHAP analysis further enhances interpretability by identifying key ultrasonographic predictors of malignancy.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145192066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Male Fetal External Genital Morphology: A Systematic Review of Sonographic Techniques and Normative Values.","authors":"Nikit Venishetty, Akash Chauhan, Ishant Goel, Jonathan Balderrama, Maral Demirjian, Lorna Kwan, Ilina D Pluym, Renea Sturm","doi":"10.1002/jum.70051","DOIUrl":"https://doi.org/10.1002/jum.70051","url":null,"abstract":"<p><p>Although commonly evaluated, fetal ultrasound assessments of penile length, width, and anogenital distance (AGD) lack standardized measurement techniques. This systematic review aimed to evaluate variability in measurement methods and generate normative growth curves for externally virilized genital development. Following PRISMA guidelines, PubMed, Google Scholar, and Web of Science were searched for studies reporting sonographic measurements of fetal penile length, width, or AGD by gestational age. Exclusion criteria included abnormal genitourinary development, intrauterine growth restriction, non-ultrasound imaging modalities, and non-English publications. Manuscripts were analyzed by a minimum of three reviewers. Data were standardized to mean dimensions and 95% confidence intervals by gestational week. Nine studies met inclusion criteria. Six measured penile length (scrotum to tip), four measured width (widest penile region), and three evaluated AGD (scrotum to anus). Resulting regression equations were y = 0.7862x - 8.4627 (length), y = 0.4209x - 3.1445 (width), and y = 1.0129x - 11.239 (AGD). Quality assessments revealed only 51.97 ± 21.5% of study parameters had a low risk of bias. The resultant growth curves provide a reference for normative development. Standardized assessment protocols and studies of anomalous development are needed to improve measurement consistency and assess the predictive value of these findings.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145186298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Intraoperative Conventional and Contrast-Enhanced Ultrasound in Brain Metastases: A Preliminary Study.","authors":"Linggang Cheng, Fumin Wang, Lu Yin, Lin Zhang, Ruijun Kang, Wei Zhang, Wen He","doi":"10.1002/jum.70047","DOIUrl":"https://doi.org/10.1002/jum.70047","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the value of intraoperative conventional and contrast-enhanced ultrasound in the surgical resection of brain metastases (BMs).</p><p><strong>Methods: </strong>A total of 46 patients with solitary tumors were enrolled. Intraoperative conventional ultrasound was performed to locate the tumors and observe the size, echo characteristics, position, depth, and degree of peritumoral edema of BMs and compare them with the findings from preoperative magnetic resonance imaging (MRI). Contrast-enhanced ultrasound was performed on 7 BMs with unclear borders to observe the borders and perfusion pattern. Completeness of tumor resection was assessed by ultrasound and compared with the assessment from postoperative MRI.</p><p><strong>Results: </strong>All BMs were detected by intraoperative conventional ultrasound. Although BMs had various ultrasonic manifestations, most of them had clear boundaries (32/46, 69.6%), severe peritumoral edema (33/46, 71.7%), and a rich blood supply (35/46, 76.1%). There was no significant difference in assessing tumor size and peritumoral edema between intraoperative conventional ultrasound and preoperative MRI (P = .121 and 1.000, respectively). BMs exhibited a rapidly inhomogeneous high enhancement and a delayed wash-out in enhancement compared with surrounding tissues on contrast-enhanced ultrasound. All the BMs had complete resection as assessed by intraoperative conventional ultrasound, which was confirmed by the postoperative MRI.</p><p><strong>Conclusions: </strong>BMs have some certain characteristics on both conventional and contrast-enhanced ultrasound. Intraoperative conventional ultrasound plays a useful role in real-time navigation and assessing the completeness of tumor resection for neurosurgeons. In addition, contrast-enhanced ultrasound is helpful in recognizing the tumor borders when the distinction between BMs and surrounding tissues is unclear.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145176185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reviews in Medical Education: A Step-by-Step Framework for Learning Transcranial Doppler Interpretation.","authors":"Ammar Jumah, Fadi Nahab, Aaron Anderson, Erika Sigman","doi":"10.1002/jum.70077","DOIUrl":"https://doi.org/10.1002/jum.70077","url":null,"abstract":"<p><p>In cerebrovascular pathologies, the transcranial Doppler (TCD) ultrasonography is a tool heavily relied on for neuromonitoring and diagnostic purposes. Interpreting TCD studies requires an in-depth understanding of the basic principles of ultrasound physics, flow hemodynamics, and the integration of TCD-related metrics. Despite the significant role TCD can play in monitoring and diagnosing cerebrovascular pathologies, many neurology trainees and other healthcare professionals receive limited instruction on interpreting TCD studies, and a few practical guides exist to support their learning. This review equips educators and learners with a structured, step-by-step approach to teaching and learning TCD interpretation, focusing on fundamental concepts and common vascular patterns that can help clinicians grasp the basics of this tool. By leveraging a clinically oriented framework, this article can support educators in neurology training programs and help bridge the educational gap in TCD interpretation.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}