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Transformers for Neuroimage Segmentation: Scoping Review.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFChronic wounds, due to their high prevalence, are a serious global health concern. Effective therapeutic strategies can significantly accelerate healing, thereby reducing the risk of complications and alleviating the economic burden on healthcare systems. Although numerous experimental studies have investigated wound healing, most rely on qualitative observations or quantitative direct measurements.
View Article and Find Full Text PDFReimagining Physician Assistant Education: Championing Cognitive Diversity to Promote Inclusivity, Neurodiversity Awareness, and a Sense of Belonging.
J Physician Assist Educ
January 2025
Tonya C. George, PhD, MSHS, MSPH, PA-C, DFAAP, is a assistant professor, Doctor of Medical Science Program, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Philadelphia.
Neurodiversity, encompassing conditions such as autism spectrum disorder, attention-deficit/hyperactivity disorder, and dyslexia, represents a significant and often under-recognized segment of the population, including within science, technology, engineering, mathematics, and medicine fields like medicine. Neurodiverse individuals possess unique skills, including enhanced creativity, analytical thinking, and meticulous attention to detail, which are valuable in health care professions. However, failure to recognize and support these individuals can result in missed opportunities, social isolation, and mental health challenges.
View Article and Find Full Text PDFQuantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.
Physiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
View Article and Find Full Text PDFWavefront aberrations in aging measured with Hartmann-Shack at 3- and 5-mm pupil sizes.
Optom Vis Sci
January 2025
School of Optometry, Indiana University, Bloomington, Indiana.
Significance: Visual acuity (VA) depends on many factors. When the goal is to assess retinal health rather than performance, then using a 3-mm pupil reduces unwanted wavefront aberrations. The axis of astigmatism can still potentially change with age.
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