Background: Many disorders of the musculoskeletal system are caused by modified net joint forces resulting from individual coping movement strategies of patients suffering from neuromuscular diseases. Purpose of this work is to introduce a personalized biomechanical model which allows the calculation of individual net joint forces via inverse dynamics based on anthropometry and kinematics of the upper extremity measured by 3D optoelectronical motion analysis.
Methods: The determined resulting net joint forces in the anatomical axis of movement may be used to explain the reason for possible malfunction of the musculoskeletal system, especially joint malformation. For example the resulting net joint forces in the humerothoracic joint from simulations are compared to a sample of children presenting obstetric brachial plexus palsy showing an internal shoulder rotation position and a sample of healthy children.
Results: The results presented from the simulation show that an increased internal shoulder rotation position leads to increased net joint forces in the humerothoracic joint. A similar behavior is presented for the subjects suffering from brachial plexus palsy with an internal shoulder rotation position.
Conclusions: The increased net joint forces are a possible reason for joint malformation in the humerothoracic joint caused by coping movements resulting from neuromuscular dysfunction as stated in literature.
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http://dx.doi.org/10.1186/1749-7221-8-10 | DOI Listing |
Phys Med Biol
January 2025
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, London, Surrey, SM2 5PT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
This study aims to develop and evaluate a fast and robust deep learningbased auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets. Approach: This study was conducted in two phases.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Department of Trauma and Reconstructive Surgery, BG Hospital Bergmanntrost, Merseburger Straße 165 06112 Halle, Halle, Sachsen-Anhalt, 06112, GERMANY.
The purpose of this study was to develop a robust deep learning approach trained with a small in-vivo MRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis. Approach: A small dataset of 15 3.0-T MRI scans from five health subjects was employed within this study.
View Article and Find Full Text PDFAm J Transl Res
December 2024
Department of Pediatrics, The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army Lanzhou 730050, Gansu, China.
Objective: To identify independent risk factors for Henoch-Schönlein purpura nephritis (HSPN) in pediatric patients.
Methods: This study enrolled 180 pediatric patients (90 with HSP, 90 with HSPN) hospitalized at the 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army from December 2022 to October 2023, with a follow-up of at least six months. Clinical data were collected at the time of the first onset of HSP.
J Am Acad Orthop Surg Glob Res Rev
January 2025
From the Department of Orthopaedic Surgery, BronxCare Health System, Bronx, NY.
Background: Rates of emergency department (ED) visits and readmissions after total joint arthroplasty (TJA) have been cited as indicators of TJA quality. Understanding the incidence and nature of these events is critical for prevention. The purpose of this study was to analyze readmission rates 30 and 90 days after TJA at a safety-net hospital in an urban setting and to compare this readmission rate with that for non-safety-net hospitals found in the current literature.
View Article and Find Full Text PDFAppl 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.
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