Skip to content

A New Method of Muscle Strength Testing Using a Quantitative Ultrasonic Technique and a Convolutional Neural Network

A New Method of Muscle Strength Testing Using a Quantitative Ultrasonic Technique and a Convolutional Neural Network

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04043832
Enrollment
80
Registered
2019-08-02
Start date
2017-05-01
Completion date
2019-06-01
Last updated
2019-08-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Muscle Disorder

Brief summary

In addition to muscle thickness and average echo intensity, this study aimed to use quantitative ultrasonic technology to increase the number of related parameters of power Doppler ultrasonography measured to describe the number, quality, and recruitment level of muscles. In addition, this method was compared with the existing muscle strength testing methods. Image recognition was performed using the traditional multivariate linear regression statistical method and the AI convolutional neural network algorithm to investigate the application of quantitative ultrasonic technology for direct evaluation of muscle strength in clinical practice.

Detailed description

Collection of all quantitative ultrasonic data was performed by one ultrasound physician. The Aixplorer® series color ultrasonic diagnostic instrument of Supersonic Imagine (France) and an L15-4 linear array probe were used. The medial head of the quadriceps muscle was measured. The subjects wore short pants that settled above the knees. Tight pants were avoided to prevent impaired venous return, which can affect the power Doppler ultrasonography result. The subjects did not exercise within half an hour before testing. Two-dimensional images of the subjects' muscles were collected in a quiet resting state and used to measure muscle thickness and the average echo intensity. The power Doppler images were used to measure the level of muscle hyperemia. The measurements were performed in the 0° knee extension position and the most prominent part of the medial head of the quadriceps muscle was measured by the ultrasound physician using visualization. The probe angle was perpendicular to the trajectory of the muscle bundle, and the measurement was performed with the muscle in a relaxed state. After the measurements, the knee was restrained to complete the isokinetic muscle strength test. When testing of the last group was completed, the knee was released immediately. The two-dimensional image and the power Doppler image at the same location were captured and the image acquisition time was no more than 3 s.

Interventions

DIAGNOSTIC_TESTquantitative ultrasonic technique

Collection of all quantitative ultrasonic data was performed by one ultrasound physician

Sponsors

Peking University Third Hospital
Lead SponsorOTHER

Study design

Observational model
OTHER
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 55 Years
Healthy volunteers
Yes

Inclusion criteria

* no major complaints of muscle numbness, spasm, or atrophy, muscle-related physical activity impairment or joint swelling and pain * no obvious malformation in the lower limbs * no disease history in the musculoskeletal system, nervous system, and peripheral blood, * no history of severe trauma in the lower limbs * joint mobility and muscle tension met the thresholds of muscle strength assessments * no severe osteoporosis * no acute inflammation or acute bone fracture repair * between 18-55 years of age

Exclusion criteria

\-

Design outcomes

Primary

MeasureTime frameDescription
Multivariate linear regression results2/6/2019The quantitative ultrasonic technology parameters (muscle thickness, average muscle echo intensity, and corrected power ultrasonic intensity) and muscle strength parameter (knee extension peak torque) were introduced into SPSS 6.0 for data processing via multivariate linear correlation analysis.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026