Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time

• Authors: Luis G Rosa, Georgia Institute of Technology, Atlanta; Jonathan S Zia, Emory University; Omer T Inan, Georgia Institute of Technology; Gregory S Sawicki, Georgia Institute of Technology
• Journal Title: PLoS One
• Volume: Volume 16, Number 5
• Publisher: Public Library of Science (PLoS) | 2021-05-26
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1371/journal.pone.0246611
• Subjects: Health Sciences, Medicine and Surgery; Computer Science
• Keywords: Machine learning; ultrasound images; muscle fascicle length

Abstract

Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for ‘in-the-loop’ applications, we evaluate accuracy of the extracted muscle length change signals against time-series’ derived from a standard, post-hoc automated tracking algorithm.