Objective: Muscle thickness is a widely used parameter for quantifying muscle function in ultrasound imaging. However, current measurement techniques generally rely on manual digitization, which is subjective, time consuming, and prone to error. The primary purposes of this study were to develop an automated muscle boundary tracking algorithm to overcome these limitations and to report its intraexaminer reliability on pectoralis major muscle.
Methods: Real-time B-mode ultrasound images of the pectoralis major muscles were acquired by an integrated data acquisition system. A transducer placement protocol was developed to facilitate better repositioning of an ultrasound transducer. Intraexaminer reliability of the tracking algorithm for static measurements was studied using intraclass correlation coefficient based on the thickness data from 11 healthy subjects recruited from a chiropractic college measured at 3 independent sessions. Standard error of measurement and minimal detectable change were calculated. Feasibility of using the tracking algorithm for dynamic measurements was also evaluated.
Results: All calculated intraclass correlation coefficients were larger than 0.96, indicating excellent reliability of the sonomyographic measurements. Minimal detectable changes were 9.7%, 6.7%, and 6.8% of the muscle thickness at the lateral, central, and medial aspects, respectively. For a 400-frame image stack with 3 pairs of 40 x 40 pixels tracking windows, the tracking took about 80 seconds to complete.
Conclusions: The tracking algorithm offers precise and reliable measurements of muscle thickness changes in clinical settings with potential to quantify the effects of a wide variety of chiropractic techniques on muscle function.
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http://dx.doi.org/10.1016/j.jmpt.2010.05.009 | DOI Listing |
EClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
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January 2025
Essex Cardiothoracic Centre, Basildon, Essex, SS16 5NL, United Kingdom; Anglia Ruskin School of Medicine & MTRC, Anglia Ruskin University, Chelmsford, Essex CM1 1SQ, United Kingdom.
Introduction: Transcatheter aortic valve replacement (TAVR) is increasingly in demand for treating severe aortic stenosis in a variety of surgical risk profiles. This means increasing wait times and elevated morbidity and mortality on the waitlist. To address this, we developed the SWIFT TAVR algorithm to prioritize patients based on clinical risk and reduce wait times.
View Article and Find Full Text PDFData Brief
December 2024
Department of Neurophysics, Philipps University Marburg, Karl-von-Frisch Straße 8a, 35043 Marburg, Hesse, Germany.
We present a comprehensive dataset comprising head- and eye-centred video recordings from human participants performing a search task in a variety of Virtual Reality (VR) environments. Using a VR motion platform, participants navigated these environments freely while their eye movements and positional data were captured and stored in CSV format. The dataset spans six distinct environments, including one specifically for calibrating the motion platform, and provides a cumulative playtime of over 10 h for both head- and eye-centred perspectives.
View Article and Find Full Text PDFSci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs).
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