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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Muscle Disorder

Intervention / Treatment
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* Diagnostic Test: quantitative ultrasonic technique


Participation Criteria
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Eligibility Criteria
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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: -

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 55 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Diagnostic Test: quantitative ultrasonic technique|Collection of all quantitative ultrasonic data was performed by one ultrasound physician|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Multivariate linear regression results | The 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. | 2/6/2019 |