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.
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 language | English |
|---|---|
| Pages (from-to) | 2567-2576 |
| Number of pages | 10 |
| Journal | Clinical neurophysiology |
| Volume | 129 |
| Issue number | 12 |
| Early online date | 27 Oct 2018 |
| DOIs | |
| Publication status | Published - 1 Dec 2018 |
Keywords
- 2019 OA procedure
- Fasciculations
- Computer-aided detection
- ALS
- Ultrasound