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http://dx.doi.org/10.3389/fpsyg.2014.00580 | DOI Listing |
Can Med Educ J
December 2024
Department of Physical Medicine and Rehabilitation, Queen's University, Ontario, Canada.
Background: Resident-focused curricula that support competency acquisition in concussion care are currently lacking. We sought to fill this gap by developing and evaluating Spiral Integrated Curricula (SIC) using the cognitive constructivism paradigm and the Utilization-Focused Evaluation (UFE) framework. The evidence-based curricula consisted of academic half-days (AHDs) and clinics for first- and second-year family medicine residents.
View Article and Find Full Text PDFEndosc Ultrasound
December 2024
Department of Gastroenterology, Ponderas Academic Hospital, Bucharest, Romania.
Background: EUS-guided fine-needle biopsy is the procedure of choice for the diagnosis of pancreatic ductal adenocarcinoma (PDAC). Nevertheless, the samples obtained are small and require expertise in pathology, whereas the diagnosis is difficult in view of the scarcity of malignant cells and the important desmoplastic reaction of these tumors. With the help of artificial intelligence, the deep learning architectures produce a fast, accurate, and automated approach for PDAC image segmentation based on whole-slide imaging.
View Article and Find Full Text PDFNutr Bull
January 2025
Queen's University Belfast, Belfast, UK.
Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system.
View Article and Find Full Text PDFMed Image Anal
January 2025
School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China; National Key Laboratory of Smart Farm Technologies and Systems, Harbin, 150001, China. Electronic address:
Despite that supervised learning has demonstrated impressive accuracy in medical image segmentation, its reliance on large labeled datasets poses a challenge due to the effort and expertise required for data acquisition. Semi-supervised learning has emerged as a potential solution. However, it tends to yield satisfactory segmentation performance in the central region of the foreground, but struggles in the edge region.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Information Technology, Uppsala University, 75237, Uppsala, Sweden.
Objectives: The aim is to assess the feasibility and accuracy of a novel quantitative ultrasound (US) method based on global speed-of-sound (g-SoS) measurement using conventional US machines, for breast density assessment in comparison to mammographic ACR (m-ACR) categories.
Materials And Methods: In a prospective study, g-SoS was assessed in the upper-outer breast quadrant of 100 women, with 92 of them also having m-ACR assessed by two radiologists across the entire breast. For g-SoS, ultrasonic waves were transmitted from varying transducer locations and the image misalignments between these were then related analytically to breast SoS.
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