Journal of Ultrasound in Medicine最新文献

{"title":"Comment on: \"Ultrasound of the Cheek: Are We Ready for Clinical Adoption?\"","authors":"Andrea Carolina Munive, Isabella Andrea Bolaños Bermúdez, Laura Nathaly Panqueva, Humberto Rivera","doi":"10.1002/jum.70269","DOIUrl":"10.1002/jum.70269","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2397"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on \"Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy\".","authors":"Madhuri Tribhuvan, Shashikant N Phatke","doi":"10.1002/jum.70275","DOIUrl":"10.1002/jum.70275","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2403-2404"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on \"Diagnostic Accuracy of Transvaginal Ultrasound for Detecting Pelvic Adhesions and Predicting Surgical Time in Benign Gynecologic Surgery\".","authors":"Minghui Gao, Zhibin Cong","doi":"10.1002/jum.70274","DOIUrl":"10.1002/jum.70274","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2401-2402"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound-MRI Fusion Deep Learning Radiomics Nomogram: Refining Prognostic Stratification for Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.","authors":"Yang Liu, Wei Wu","doi":"10.1002/jum.70268","DOIUrl":"10.1002/jum.70268","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2395-2396"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147717119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Cross-Platform Generalizability and Clinical Decision-Making in Deep Learning-Based Detection of Ultrasound Transducer Uniformity Artifacts.","authors":"Jiaxin Cai","doi":"10.1002/jum.70273","DOIUrl":"10.1002/jum.70273","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2399"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-Assisted Wireless Handheld Ultrasound for Screening Developmental Dysplasia of the Hip in Infants.","authors":"Dandan Zhang, Simiao Tao, Ping Chen, Jiangtao Wang, Yu Yang, Xiuxiu Zhang, Tieqiang Wang, Hailin Pan, Jiong Chen, Baoying Ye","doi":"10.1002/jum.70318","DOIUrl":"10.1002/jum.70318","url":null,"abstract":"<p><strong>Objectives: </strong>Ultrasonography is increasingly the preferred method for infant hip screening to enable timely diagnosis and treatment of developmental dysplasia of the hip (DDH). However, its reliance on experienced specialists and bulky equipment limits its application in routine screening, particularly in resource-limited and remote settings. We aimed to develop an artificial intelligence wireless handheld ultrasound hip diagnostic system (AI-HUDs) for accurate and robust DDH diagnosis.</p><p><strong>Methods: </strong>An AI-HUDs was developed to automatically identify and analyze 6 key anatomical landmarks in static and dynamic hip ultrasound images, employing the Graf method to measure α and β angles. The dataset comprised 1192 ultrasound images and 498 dynamic videos. The YOLOv8 model was trained and tested to construct AI-HUDs. Evaluation focused on the consistency between AI-HUDs' automatic angle measurements and manual measurements by experts using traditional ultrasound equipment. Additionally, 104 hip ultrasound videos from 52 infants were acquired by a resident physician using AI-HUDs, while corresponding static images were manually measured by ultrasound experts to assess agreement between novice-operated AI-HUDs and expert manual assessment.</p><p><strong>Results: </strong>Compared with expert manual measurements, the AI-HUDs demonstrated good performance in static mode: the differences in α and β angles exhibited standard deviations of 1.38° and 2.14°, and mean absolute errors (MAEs) of 1.05° and 1.65°, respectively. The intraclass correlation coefficients (ICCs) were 0.82 (α) and 0.64 (β). In dynamic mode, the MAEs increased slightly to 1.18° (α) and 1.86° (β), while the ICCs decreased to 0.77 for α and 0.51 for β. When operated by the resident physician, AI-HUDs maintained good performance with expert manual measurements: MAEs were 1.16° (α) and 1.91° (β); ICCs were 0.69 (α) and 0.61 (β). Bland-Altman analysis showed 96.15% (α) and 95.19% (β) of data points within the limits of agreement.</p><p><strong>Conclusion: </strong>AI-HUDs demonstrated high reliability and accuracy in automatically measuring α and β angles. Resident physicians using this system achieved diagnostic performance comparable to experts. AI-HUDs show promise as a convenient DDH screening tool in resource-scarce regions, potentially facilitating wider adoption of infant hip ultrasound screening.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2329-2343"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148277736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor Concerning \"Bedside Ultrasound Assessment to Detect Muscle Loss in Critically Ill Children\".","authors":"Esra Türe, Ahmet Kağan Özkaya, Elif Nur İldeş","doi":"10.1002/jum.70277","DOIUrl":"10.1002/jum.70277","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2407-2408"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological Considerations in the Evaluation of Thymic-Thoracic Ratio in Fetuses With Congenital Heart Defects.","authors":"Yakup Ozbay","doi":"10.1002/jum.70278","DOIUrl":"10.1002/jum.70278","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2409-2410"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Ultrasonic Local Pulse Wave Velocity Estimation Via Optimal Time Fiduciary Point Combination and Time Delays Fitness.","authors":"Li Xiong, Yufeng Zhang, Xiaoxu Wang, Bingbing He, Zhiyao Li, Jingying Zhu","doi":"10.1002/jum.70328","DOIUrl":"10.1002/jum.70328","url":null,"abstract":"<p><p>Local pulse wave velocity (PWV) is key for quantifying arteriosclerosis progression, vital for early cerebrovascular diseases diagnosis and prevention. The ultrasonic transit time (TT) method calculates PWV by fitting time delays (TDs) of pulse wave (PW) propagation with the known distances. However, reflected waves and noise can bias the time fiduciary point (TFP) positioning, reducing the fitting performance and estimation accuracy. This work proposes a PGC (PSO-GA and Cook's distance) method using optimal multi-TFP (MTFP) combination and TDs fitness via PSO-GA for enhancing the TT-based local PWV estimation. First, PWs are calculated, and then a MTFP combination is randomly selected to calculate the TDs, whose outliers are detected by Cook's distance with a randomly selected threshold. Thereafter, the reciprocal of coefficient of determination for fitting is used as the fitness function to optimally update the MTFP combination and Cook's distance threshold via PSO-GA. Finally, the optimal MTFP and Cook's distance threshold are obtained to compute the improved PWV. The proposed PGC method is compared with the traditional method without optimal processing. The results show that the PGC-based mean and standard deviation of the normalized root mean squared errors between the estimated and preset PWVs decreased from 10.28 ± 2.51 to 5.96 ± 1.41% in simulation experiments, and the coefficient of variation for the measured PWVs decreased from 11.87 to 8.53% in in vivo experiments. In conclusion, the PGC method demonstrated improved accuracy in simulations and enhanced repeatability with reduced variability in vivo, offering the potential to provide more accurate diagnostic information for cerebrovascular diseases.</p>","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2353-2370"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148218213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Ultrasound-Based Diagnosis of Pelvic Adhesions: From Traditional Sliding Sign to Machine Learning-Enhanced Detection.","authors":"Jiaxin Cai","doi":"10.1002/jum.70267","DOIUrl":"10.1002/jum.70267","url":null,"abstract":"","PeriodicalId":17563,"journal":{"name":"Journal of Ultrasound in Medicine","volume":" ","pages":"2393"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147775344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}