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Objectives: Training healthcare physicians to perform intestinal ultrasound (IUS) during outpatient visits with equal accuracy as radiologists could improve clinical management of IBD patients. We aimed to assess whether a healthcare-physician can be trained to perform IUS, with equal accuracy compared with experienced radiologists in children with iBD, and to assess inter-observer agreement.
Methods: Consecutive children, 6 to 18 years with IBD or suspicion of IBD, who underwent ileo-colonoscopy were enrolled. iUS was performed independently by a trained healthcare-physician and a radiologist in 1 visit. Training existed of an international training curriculum for IUS. Operators were blinded for each other's IUS, and for the ileocolonoscopy. Difference in accuracy of IUS by the healthcare-physician and radiologist was assessed using areas under the ROC curve (AUROC). Inter-observer variability was assessed in terminal ileum (TI), transverse colon (TC) and descending-colon (DC), for disease activity (ie, bowel wall thickness [BWT] >2 mm with hyperaemia or fat-proliferation, or BWT >3 mm).
Results: We included 73 patients (median age 15, interquartile range [IQR]:13-17, 37 [51%] female, 43 [58%] with Crohn disease). AUROC ranged between 0.71 and 0.81 for the healthcare-physician and between 0.67 and 0.79 for radiologist (P > 0.05). Inter-observer agreement for disease activity per segment was moderate (K: 0.58 [SE: 0.09], 0.49 [SE: 0.12], 0.52 [SE: 0.11] respectively for TI, TC, and DC).
Conclusions: A healthcare- physician can be trained to perform IUS in children with IBD with comparable diagnostic accuracy as experienced radiologists. The interobserver agreement is moderate. Our findings support the usage of IUS in clinical management of children with IBD.
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http://dx.doi.org/10.1097/MPG.0000000000003442 | DOI Listing |
Neural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFNeural Netw
September 2025
Shanghai Maritime University, Shanghai, 201306, China. Electronic address:
Cross-modal hashing aims to leverage hashing functions to map multimodal data into a unified low-dimensional space, realizing efficient cross-modal retrieval. In particular, unsupervised cross-modal hashing methods attract significant attention for not needing external label information. However, in the field of unsupervised cross-modal hashing, there are several pressing issues to address: (1) how to facilitate semantic alignment between modalities, and (2) how to effectively capture the intrinsic relationships between data, thereby constructing a more reliable affinity matrix to assist in the learning of hash codes.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
View Article and Find Full Text PDFJ Med Internet Res
September 2025
School of Governance and Policy Science, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong).
Background: Older adults are more vulnerable to severe consequences caused by seasonal influenza. Although seasonal influenza vaccination (SIV) is effective and free vaccines are available, the SIV uptake rate remained inadequate among people aged 65 years or older in Hong Kong, China. There was a lack of studies evaluating ChatGPT in promoting vaccination uptake among older adults.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Chulalongkorn University, Bangkok, Thailand.
Background: The interprofessional educational curriculum for patient and personnel safety is of critical importance, especially in the context of the COVID-19 pandemic, to prepare junior multiprofessional teams for emergency settings.
Objective: This study aimed to evaluate the effectiveness of an innovative interprofessional educational curriculum that integrated medical movies, massive open online courses (MOOCs), and 3D computer-based or virtual reality (VR) simulation-based interprofessional education (SimBIE) with team co-debriefing to enhance interprofessional collaboration and team performance using Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS). This study addressed 3 key questions.