Computer-aided detection of fasciculations and other movements in muscle with ultrasound: Development and clinical application

Kaj Gijsbertse, Max Bakker, André Sprengers, Juerd Wijntjes, Saskia Lassche, Nico Verdonschot, Chris L. de Korte, Nens van Alfen

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
227 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)2567-2576
Number of pages10
JournalClinical neurophysiology
Issue number12
Early online date27 Oct 2018
Publication statusPublished - 1 Dec 2018


  • UT-Hybrid-D
  • Fasciculations
  • Computer-aided detection
  • ALS
  • Ultrasound


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