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Enhancing Access to Neuraxial Ultrasound Phantoms for Medical Education of Pediatric Anesthesia Trainees: Tutorial

Enhancing Access to Neuraxial Ultrasound Phantoms for Medical Education of Pediatric Anesthesia Trainees: Tutorial

Ultrasound enhances safety, decreases complications, and improves the efficacy and accuracy of neuraxial blockade in pediatric patients from preterm to adolescence [5-12]. The utility of ultrasound is even more apparent in syndromic children with unusual anatomy, patients who comprise a large subset of the pediatric population that presents for surgery at a young age [13]. Honing pediatric patient–related ultrasound skills in a simulation setting is an ideal scenario for learning without risk.

Leah Webb, Melissa Masaracchia, Kim Strupp

JMIR Med Educ 2025;11:e63682

Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

Diagnosis of Sarcopenia Using Convolutional Neural Network Models Based on Muscle Ultrasound Images: Prospective Multicenter Study

The ultrasound equipment and transducer of the centers are shown in Multimedia Appendix 2. The ultrasound equipment in the training and internal validation cohort was the Aixplorer Ultrasound system (Super Sonic Imagine), with an SL 10-2 multifrequency linear transducer.

Zi-Tong Chen, Xiao-Long Li, Feng-Shan Jin, Yi-Lei Shi, Lei Zhang, Hao-Hao Yin, Yu-Li Zhu, Xin-Yi Tang, Xi-Yuan Lin, Bei-Lei Lu, Qun Wang, Li-Ping Sun, Xiao-Xiang Zhu, Li Qiu, Hui-Xiong Xu, Le-Hang Guo

J Med Internet Res 2025;27:e70545

Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study

Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study

As one of the most important clinical documents, radiology reports record essential information from patient imaging data such as computed tomography (CT) scans, positron emission tomography (PET) scans, magnetic resonance imaging, x-rays, and ultrasound (US) examinations. These reports typically consist of two main sections, namely findings and impressions.

Danqing Hu, Shanyuan Zhang, Qing Liu, Xiaofeng Zhu, Bing Liu

J Med Internet Res 2025;27:e65547

Performance Evaluation and Implications of Large Language Models in Radiology Board Exams: Prospective Comparative Analysis

Performance Evaluation and Implications of Large Language Models in Radiology Board Exams: Prospective Comparative Analysis

Each question was individually reviewed and validated by two academic radiologists—one specializing in ultrasound with 20 years of experience and the other in abdominal radiology with 4 years of experience. Questions were only included if both reviewers concurred on their relevance and appropriateness for this study. Questions that involved images were excluded.

Boxiong Wei

JMIR Med Educ 2025;11:e64284

Performance of Artificial Intelligence Chatbots on Ultrasound Examinations: Cross-Sectional Comparative Analysis

Performance of Artificial Intelligence Chatbots on Ultrasound Examinations: Cross-Sectional Comparative Analysis

With the rapid development of ultrasound medicine, the demand for ultrasound examination is increasing, and the teaching and popularization of ultrasound is limited. An increasing number of junior ultrasound doctors, students, and patients have begun to use chatbots to obtain ultrasound-related consultation and answers.

Yong Zhang, Xiao Lu, Yan Luo, Ying Zhu, Wenwu Ling

JMIR Med Inform 2025;13:e63924

Advancing Digital Education Technologies by Empowering Nurses With Point-of-Care Ultrasound: Protocol for a Mixed Methods Study

Advancing Digital Education Technologies by Empowering Nurses With Point-of-Care Ultrasound: Protocol for a Mixed Methods Study

Defined as both a portable bedside tool for immediate diagnostic or therapeutic use and a means for the instantaneous acquisition, interpretation, and clinical integration of ultrasound images by health care professionals, Po CUS has transcended its traditional medical confines.

Fernanda Raphael Escobar Gimenes, Angelita Maria Stabile, Rodrigo Magri Bernardes, Vinicius Batista Santos, Mayra Gonçalves Menegueti, Patricia Rezende do Prado, Mauricio Serra Ribeiro, Flavia Giron Camerini, Soraia Assad Nasbine Rabeh

JMIR Res Protoc 2024;13:e58030

Artificial Intelligence–Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review

Artificial Intelligence–Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review

Specifically, in support of diagnostics and therapeutics recommendations for ectopic pregnancy, ontology has been used for supporting the annotation of medical images (eg, ultrasound images for obstetrics) [26,27]. Arden syntax was used to formalize obstetric clinical guidelines into a knowledge base that supports CDSS functions for obstetrics [29]. XML was used for encoding a knowledge base that underpins mobile app–based CDSS for prenatal care [34].

Xinnian Lin, Chen Liang, Jihong Liu, Tianchu Lyu, Nadia Ghumman, Berry Campbell

J Med Internet Res 2024;26:e54737

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis

Point-of-care ultrasound (POCUS) is a dynamic medical imaging technique used at patients’ bedside to make accurate, real-time diagnoses [22-24]. Though POCUS has significant value in health care settings, advanced training is required to accurately apply this tool to clinical care [25]. As such, ML models that automate POCUS image interpretation hold exceptional potential clinical value.

Nicole M Duggan, Mike Jin, Maria Alejandra Duran Mendicuti, Stephen Hallisey, Denie Bernier, Lauren A Selame, Ameneh Asgari-Targhi, Chanel E Fischetti, Ruben Lucassen, Anthony E Samir, Erik Duhaime, Tina Kapur, Andrew J Goldsmith

J Med Internet Res 2024;26:e51397

Ultrasound-Assisted Continence Care Support in an Inpatient Care Setting: Protocol for a Pilot Implementation Study

Ultrasound-Assisted Continence Care Support in an Inpatient Care Setting: Protocol for a Pilot Implementation Study

The DFree Ultrasound Sensor (Triple W Japan KK) is a DAT that comprises 2 parts: a controller and an ultrasound probe that has the potential to predict the need for voiding (Figure 2). DFree is a supportive tool for patients with bladder dysfunction. The basic principle of the sensor is based on measuring the degree of bladder expansion during filling by using ultrasound measurements, which then notifies nursing professionals through a signal emitted through an i OS operating system app.

Sebastian Hofstetter, Madeleine Ritter-Herschbach, Dominik Behr, Patrick Jahn

JMIR Res Protoc 2023;12:e47025