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Ground Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks.
Appl Sci (Basel)
June 2024
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.
View Article and Find Full Text PDFClassification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19.
Int J Telemed Appl
January 2025
Medical Familiar Unit, Instituto de Seguridad y Servicios Sociales de Los Trabajadores del Estado, Torreón, Coahuila, Mexico.
This study proposes an automated system for assessing lung damage severity in coronavirus disease 2019 (COVID-19) patients using computed tomography (CT) images. These preprocessed CT images identify the extent of pulmonary parenchyma (PP) and ground-glass opacity and pulmonary infiltrates (GGO-PIs). Two types of images-saliency () image and discrete cosine transform (DCT) energy image-were generated from these images.
View Article and Find Full Text PDFDeep cascaded registration and weakly-supervised segmentation of fetal brain MRI.
Heliyon
January 2025
BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.
View Article and Find Full Text PDFAutomated analysis for glaucoma screening of retinal videos acquired with smartphone-based ophthalmoscope.
Heliyon
July 2024
D-Eye Srl, Padova, 35131, Italy.
Widespread screening is crucial for the early diagnosis and treatment of glaucoma, the leading cause of visual impairment and blindness. The development of portable technologies, such as smartphone-based ophthalmoscopes, able to image the optical nerve head, represents a resource for large-scale glaucoma screening. Indeed, they consist of an optical device attached to a common smartphone, making the overall device cheap and easy to use.
View Article and Find Full Text PDFHyper-brain independent component analysis (HB-ICA): an approach for detecting inter-brain networks from fNIRS-hyperscanning data.
Biomed Opt Express
January 2025
School of Psychology, Shenzhen University, Shenzhen, China.
Functional near-infrared spectroscopy (fNIRS) -based hyperscanning is a popular new technology in the field of social neuroscience research. In recent years, studying human social interaction from the perspective of inter-brain networks has received increasing attention. In the present study, we proposed a new approach named the hyper-brain independent component analysis (HB-ICA) for detecting the inter-brain networks from fNIRS-hyperscanning data.
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