Purpose: Scientific validity is questionable when findings from studies cannot be used to make sense of physiological and/or biomechanical data. In particular, is the case of in vivo determination of tendon stiffness (K). Here, approaches range from taking the gradient (a) throughout the data range of resting to Maximal Voluntary Contraction (MVC), (b) tangents at individual data points, (c) linear regressions at discrete force levels ((b) and (c) being 'reference standard' as they utilise a number of distinct regions of the Force-Elongation Relationship (FER)).
Study Design: A mathematical model approach is used to develop simple curvilinear FERs as seen when determining tendon mechanical properties, to allow variable calculations of K.
Objectives: To compare variability in K estimates using the various approaches currently seen in the literature.
Methods: Three FER models were developed, representing low, medium and high K. Values of K were determined and compared using the approaches reported in the literature to estimate the magnitude of the difference between values attained of K.
Results: Through mathematical modelling, we demonstrate that the impact on the recorded value of K is substantial: relative to the reference standard methods, computation methods published range from underestimating K by 26% to overestimating it by 51%.
Conclusion: This modelling helps by providing a 'scaling factor' through which the between studies variability associated with computational methods differences is minimised. This is especially important where researchers or clinicians require values which are consistent in the context of establishing the 'true' tendon mechanical properties to inform models or materials based on the biological properties of the human tendon.
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http://dx.doi.org/10.1016/j.jmbbm.2011.08.008 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Med Phys
January 2025
Department of Engineering Physics, Tsinghua University, Beijing, China.
Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging.
View Article and Find Full Text PDFOral Radiol
January 2025
Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Istanbul University, Istanbul, Turkey.
Objectives: This study evaluates the potential of pulp volume/total tooth-volume measurements of canine teeth in relation to chronologic age in patients with cleft lip and palate (CLP). The significance of this study lies in its exploration of the usability of these measurements for age determination in CLP patients, providing a novel perspective to the existing literature.
Methods: Cone beam computed tomography images of 33 patients (16 females, 17 males) with unilateral CLP aged 14-45 years and 33 age- and sex-matched healthy individuals (16 females, 17 males) were retrospectively evaluated.
J Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
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