The use of videoconferencing and file sharing tools among colleagues at large institutions has traditionally been confounded by differing security policies in place at each location. Building an electronic collaboration infrastructure based upon the lowest-common-denominator security configuration expected to be encountered will yield an accessible collaboration environment previously unavailable to geographically diverse researchers and clinicians.
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Generative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Fieldwork on animals living in extreme conditions as a source of biomedical innovation.
Sci One Health
November 2024
CR University Grenoble Alpes, Institute for Advanced Biosciences, Inserm U1209, CNRS UMR 5309, Grenoble, France.
Most biomedical research on animals is based on the handful of the so-called standard model organisms, i.e. laboratory mice, rats or , but the keys to some important biomedical questions may simply not be found in these.
View Article and Find Full Text PDFRisk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis.
Front Nutr
January 2025
Department of Epidemiology and Health Statistics, Tianjin Medical University, Tianjin, China.
Background: Although more risk prediction models are available for feeding intolerance in enteral-nourishment patients, it is still unclear how well these models will work in clinical settings. Future research faces challenges in validating model accuracy across populations, enhancing interpretability for clinical use, and overcoming dataset limitations.
Objective: To thoroughly examine studies that have been published on feeding intolerance risk prediction models for enteral nutrition patients.
Clinical Outcomes after Revascularization in Patients with Chronic Limb-Threatening Ischemia.
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January 2025
Department of Vascular Surgery, Aichi Medical University, Nagakute, Aichi, Japan.
Chronic limb-threatening ischemia (CLTI) occurs in the advanced stage of peripheral artery disease and is associated with high risks of mortality and amputation. Universal management strategies are not always applicable, owing to population diversity, and the Western trials may not be applicable to Japanese patients, owing to differences in demographics and clinical profiles. This paper examines the outcomes of revascularization in Japanese CLTI patients and emphasizes the benefits of tailored management.
View Article and Find Full Text PDFMagnetic resonance imaging in traumatic brain injury: a survey of clinical practitioners' experiences and views on current practice and obstacles.
Brain Inj
January 2025
Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
Introduction: Magnetic resonance imaging (MRI) has revolutionized our capacity to examine brain alterations in traumatic brain injury (TBI). However, little is known about the level of implementation of MRI techniques in clinical practice in TBI and associated obstacles.
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