Commonly available Pulsed Wave Doppler (PW) flow velocity measurements for Ultrasound (US) investigation require the operator to manually set the direction of the flow velocity vector along the blood vessel axis on the US image, in order to determine the Doppler angle and then to estimate the real flow velocity. The present work investigates the possibility to implement on a commercially available US system an innovative Automatic Doppler Angle (ADA) Technology by analyzing the best workflow in terms of higher execution speed, lower keystrokes/adjustments helping in the prevention of Work-related Musculoskeletal Disorders (WRMSD) and a Doppler angle correction precision, comparable to the one obtained manually by expert sonographer. Ergonomics and workflow tests, then accuracy and repeatability evaluations of the Doppler velocity measurement, were performed on a portable US system (MyLabAlpha, Esaote S.p.A., Florence, Italy) by an expert sonographer. Ergonomics and workflow Tests were performed to analyze the potential of ADA in terms of reduction of muscular activation applied (by SEMG), number of activations (by cameras optoelectronic system) and time needed using ADA, in comparison to manual procedure. Accuracy and intra-operator repeatability tests of the velocity measurement were performed to evaluate the precision of the obtained PW trace velocity measurements with ADA technology, compared to manual ones. Results provided evidence that ADA tool allowed: a reduction of muscular activation (from 12% for trapezius descendens, to 25% for deltoideus anterior) a lower total number of keystrokes and a reduction of the US scan time of about 56%. The maximal variation between PW Doppler trace velocity measurement set automatically by ADA and set manually by sonographer was 11%. ADA technology can provide a Doppler angle correction precision comparable to the manual one, while decreasing the risk of WRMSD.
- Blood velocity
- Common carotid artery
- Surface electromyography
- Work-related musculoskeletal disorders
ASJC Scopus subject areas
- Artificial Intelligence
- Industrial and Manufacturing Engineering