Kaj Gijsbertse1, Max Bakker2, André Sprengers3, Juerd Wijntjes4, Saskia Lassche4, Nico Verdonschot3, Chris L de Korte5, Nens van Alfen4. 1. Orthopaedic Research Laboratory, Department of Orthopaedics, Radboud University Medical Center, Nijmegen, The Netherlands. 2. Orthopaedic Research Laboratory, Department of Orthopaedics, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: Max.Bakker@radboudumc.nl. 3. Orthopaedic Research Laboratory, Department of Orthopaedics, Radboud University Medical Center, Nijmegen, The Netherlands; Laboratory of Biomechanical Engineering, University of Twente, Enschede, The Netherlands. 4. Department of Neurology and Clinical Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands. 5. Medical Ultrasound Imaging Center (MUSIC), Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Physics of Fluids Group, MESA+ Institute for Nanotechnology and MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands.
Abstract
OBJECTIVE: To develop an automated algorithm for detecting fasciculations and other movements in muscle ultrasound videos. Fasciculation detection in muscle ultrasound is routinely performed online by observing the live videos. However, human observation limits the objective information gained. Automated detection of movement is expected to improved sensitivity and specificity and increase reliability. METHODS: We used 42 ultrasound videos from 11 neuromuscular patients for an iterative learning process between human observers and automated computer analysis, to identify muscle ultrasound movements. Two different datasets were selected from this, one to develop the algorithm and one to validate it. The outcome was compared to manual movement identification by clinicians. The algorithm also quantifies specific parameters of different movement types, to enable automated differentiation of events. RESULTS: The algorithm reliably detected fasciculations. With algorithm guidance, observers found more fasciculations compared to visual analysis alone, and prescreening the videos with the algorithm saved clinicians significant time compared to reviewing full video sequences. All videos also contained other movements, especially contraction pseudotremor, which confused human interpretation in some. CONCLUSIONS: Automated movement detection is a feasible and attractive method to screen for fasciculations in muscle ultrasound videos. SIGNIFICANCE: Our findings affirm the potential clinical usefulness of automated movement analysis in muscle ultrasound.
OBJECTIVE: To develop an automated algorithm for detecting fasciculations and other movements in muscle ultrasound videos. Fasciculation detection in muscle ultrasound is routinely performed online by observing the live videos. However, human observation limits the objective information gained. Automated detection of movement is expected to improved sensitivity and specificity and increase reliability. METHODS: We used 42 ultrasound videos from 11 neuromuscular patients for an iterative learning process between human observers and automated computer analysis, to identify muscle ultrasound movements. Two different datasets were selected from this, one to develop the algorithm and one to validate it. The outcome was compared to manual movement identification by clinicians. The algorithm also quantifies specific parameters of different movement types, to enable automated differentiation of events. RESULTS: The algorithm reliably detected fasciculations. With algorithm guidance, observers found more fasciculations compared to visual analysis alone, and prescreening the videos with the algorithm saved clinicians significant time compared to reviewing full video sequences. All videos also contained other movements, especially contraction pseudotremor, which confused human interpretation in some. CONCLUSIONS: Automated movement detection is a feasible and attractive method to screen for fasciculations in muscle ultrasound videos. SIGNIFICANCE: Our findings affirm the potential clinical usefulness of automated movement analysis in muscle ultrasound.
Authors: Juerd Wijntjes; Joris van der Hoeven; Christiaan G J Saris; Jonne Doorduin; Nens van Alfen Journal: Muscle Nerve Date: 2022-07-16 Impact factor: 3.852