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http://dx.doi.org/10.26633/RPSP.2017.172 | DOI Listing |
BMC Musculoskelet Disord
January 2025
Department of Health Sciences, Faculty of Medicine, Lund University, Box 117, Lund, 221 00, Sweden.
Background: Osteoarthritis (OA) often leads to pain and functional limitations, impacting work and daily life. Physical activity (PA) is an important part of the treatment. Wearable activity trackers (WATs) offer a novel approach to promote PA but could also aid in finding a sustainable PA level over time.
View Article and Find Full Text PDFPlant Methods
January 2025
School of Electronic and Information Engineering, Liaoning Technical University, Huludao, 125105, China.
Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques.
View Article and Find Full Text PDFBMC Public Health
January 2025
Health & Nutrition Cluster, Institute of Development Studies, University of Sussex, Brighton, UK.
Background: Global re-emergence of the zoonotic viral disease, Mpox (Monkeypox) has drawn global attention, leading to its declaration as a Public Health Emergency of International Concern (PHEIC) by World Health Organisation (WHO) in July 2022. Nigeria is a spotlight identified for the viral disease outbreak, with attention drawn on its transmission to non-endemic nations. With the country's healthcare challenges, care seeking practices particularly amongst low-income urban informal settlement populations are diverse - presenting challenges to both case identification and management during an outbreak.
View Article and Find Full Text PDFInt J Cardiovasc Imaging
January 2025
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, UC Davis School of Medicine, University of California, Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817-2307, USA.
Purpose: To explore the information in routine digital subtraction angiography (DSA) and evaluate deep learning algorithms for automated identification of anatomic location in DSA sequences.
Methods: DSA of the abdominal aorta, celiac, superior mesenteric, inferior mesenteric, and bilateral external iliac arteries was labeled with the anatomic location from retrospectively collected endovascular procedures performed between 2010 and 2020 at a tertiary care medical center. "Key" images within each sequence demonstrating the parent vessel and the first bifurcation were additionally labeled.
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