Assessment System for Sarcopenia Based on Ultrasonographic Data

An Artificial Intelligence Assessment System for Risk Grading of Sarcopenia Based on Ultrasonographic Multidimensional Data

1. To develop an artificial intelligence assisted diagnostic model for sarcopenia based on ultrasound images;
2. To develop artificial intelligence classification and regression models for auxiliary diagnosis of sarcopenia, patient strength estimation, and other functions based on ultrasound image data.

Study Overview

• Status: Recruiting
• Conditions: Sarcopenia; Ultrasound
• Intervention / Treatment: Diagnostic test: ultrasound scan

Detailed Description

Sarcopenia is a syndrome of age-related muscle mass loss and muscle function decrease, which can be comorbid with a variety of diseases and interacts extensively with various disease states to influence disease prognosis. Diseases such as cancer, diabetes, chronic kidney disease, and rheumatoid arthritis can accelerate the process of muscle loss by affecting myogenic cell regeneration, interfering with protein synthesis, increasing protein consumption, and enhancing protein degradation by the ubiquitination pathway, and the decline in motor function will, in turn, further worsen the prognosis of the disease. Despite some regional differences, the prevalence of sarcopenia has been found to exceed 10%. Early identification of the potential risk of sarcopenia and early intervention in the early stages of muscle mass and function impairment is one of the most important steps to improve the quality of life of older adults.

Currently, the diagnosis of sarcopenia relies on three features: loss of muscle mass, loss of muscle strength, and loss of physical performance. At present, physicians usually use bioelectrical impedance analysis (BIA) or dual-energy X-ray absorptiometry (DXA) to determine skeletal muscle mass index SMI to measure muscle mass, grip strength test to measure muscle strength, gait speed or tools such as SPPB scores to assess physical performance. A diagnosis of sarcopenia can be made when a subject experiences a decrease in SMI combined with a decrease in grip strength or a decrease in gait speed.

In the field of medical imaging, researchers have been working to explore and validate appropriate imaging tools and markers to diagnose and evaluate sarcopenia. The common methods for deep mining of medical imaging include radiomics and machine learning, usually by analyzing the texture features of muscles at specific sites to quantify muscle function or segmenting skeletal muscles accurately in two dimensions or three dimensions to quantify muscle mass. Compared to computed tomography (CT) or magnetic resonance imaging (MRI), ultrasound is a more accessible and less costly medical imaging technique, especially in low- and middle-income regions. Ultrasound can be used to conveniently scan local muscles and obtain muscle characteristics such as muscle thickness, cross-sectional area, and pennation angle. Our previous studies have demonstrated that SMI in older adults can be accurately estimated by using muscle thickness at four sites together with basic information such as age and body mass index (BMI), and have found in cross-regional validation that the stability of estimates can be maintained across communities with very different ethnic proportions. However, several existing large studies on ultrasound in sarcopenia are currently focusing only on muscle morphological measurements, ignoring the large amount of hidden ultrasound image information. At the same time, the flexibility of the scanning process has led to greater resistance from radiomics or deep learning tools to use the images for artificial intelligence classification than CT or MRI.

Fronted with such a dilemma, we attempted to establish an intelligent risk grading system for sarcopenia, based on multidimensional data including basic information such as age and BMI, ultrasound measurements, and original image content, to complete the risk grading of sarcopenia in older adults in a one-stop manner, so as to realize the rapid screening and classification of potential sarcopenia populations for further clinical management.

• Study Type: Observational
• Enrollment (Estimated): 1500

Contacts and Locations

• Study Contact: Name: Xinyi Tang, Dr.; Phone Number: +8615680819215; Email: tangxinyi1996@outlook.com

Study Locations

• China
  • Sichuan
    • Chengdu, Sichuan, China
      • Recruiting
      • Xinyi Tang
      • Contact: Xinyi Tang, Dr.; Phone Number: +8615680819215

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

The older adults with risk of sarcopenia

Description

Inclusion Criteria:

• > 50 years of age
• Patients with suspected sarcopenia, for example, who needed assistance with walking, rising from a chair, or climbing stairs; recently had a history of falls walking; recent unintentional weight loss

Exclusion Criteria:

• Amputated arm or leg
• Severe oedema (oedema higher than knee level)
• Implantable pacemaker
• Impaired consciousness, poor general health, or other reasons that would prevent the individual from completing the study

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Hospitalized older adults at risk of sarcopenia	Diagnostic test: ultrasound scan; ultrasound scan
Community-dwelling older adults at risk of sarcopenia	Diagnostic test: ultrasound scan; ultrasound scan

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Death		Within 2 years after the initial ultrasound examination
Diagnosed sarcopenia	Skeletal muscle mass index (SMI)<7 (men) /5.7 (women)kg/m2 (measured by BIA) and gait speed<1m/s or grip strength<28kg (men)/18kg (women)	Within 2 years after the initial ultrasound examination

Collaborators and Investigators

• Sponsor: West China Hospital
• Investigators: Principal Investigator: Xinyi Tang, Dr., Department of Medical Ultrasound, West China Hospital, Sichuan University

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2020
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2028

Study Registration Dates

• First Submitted: December 28, 2023
• First Submitted That Met QC Criteria: December 28, 2023
• First Posted (Actual): January 10, 2024

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: March 11, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neurologic Manifestations; Nervous System Diseases; Neuromuscular Manifestations; Pathological Conditions, Anatomical; Muscular Atrophy; Atrophy; Sarcopenia
• Other Study ID Numbers: XTang-0001

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: By submitting an application to the project leader via email and obtaining approval from relevant hospital departments

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No