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Age group classification based on optical measurement of brain pulsation using machine learning.
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
View Article and Find Full Text PDFDeep Learning-Based Glioma Segmentation of 2D Intraoperative Ultrasound Images: A Multicenter Study Using the Brain Tumor Intraoperative Ultrasound Database (BraTioUS).
Cancers (Basel)
January 2025
Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain.
Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset.
View Article and Find Full Text PDFAssessment of scattered and leakage radiation from ultra-portable X-ray systems in chest imaging: An independent study.
PLOS Glob Public Health
January 2025
Médecins Sans Frontières, International, Geneva, Switzerland.
Ultraportable (UP) X-ray devices are ideal to use in community-based settings, particularly for chest X-ray (CXR) screening of tuberculosis (TB). Unfortunately, there is insufficient guidance on the radiation safety of these devices. This study aims to determine the radiation dose by UP X-ray devices to both the public and radiographers compared to international dose limits.
View Article and Find Full Text PDFClassification and Prediction of VOCs Using an IoT-Enabled Electronic Nose System-Based Lab Prototype for Breath Sensing Applications.
ACS Sens
January 2025
Interdisciplinary Research Division Smart HealthCare, Indian Institute of Technology Jodhpur, Jodhpur 342030, India.
Electronic nose (e-nose) systems are well known in breath analysis because they combine breath printing with advanced and intelligent machine learning (ML) algorithms. This work demonstrates development of an e-nose system comprising gas sensors exposed to six different volatile organic compounds (VOCs). The change in the voltage of the sensors was recorded and analyzed through ML algorithms to achieve selectivity and predict the VOCs.
View Article and Find Full Text PDFValidity of a New Portable Sensor to Measure Velocity-Based Resistance Training.
Methods Protoc
January 2025
Faculty of Health Science, International University Isabel I of Castile, 09003 Burgos, Spain.
This study evaluated the concurrent validity of the Vitruve linear encoder compared to the T-Force device for measuring mean propulsive velocity (MPV) and peak velocity (PV) during the free-weight bench press exercise. Thirteen resistance-trained men participated in three sessions, during which MPV and PV were recorded simultaneously by both devices. The data were analysed using one-way ANOVA, Pearson's correlation, Bland-Altman analysis, and effect size calculations, with statistical significance set at ≤ 0.
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