Application of Artificial Intelligence Algorithm Based on CT Imaging for Muscle Parameter Measurement

Application of Artificial Intelligence Algorithm Based on CT Imaging for Muscle Parameter Measurement in the Diagnosis of Sarcopenia

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06845462

Enrollment

1080

Registered

2025-02-25

Start date

2023-09-05

Completion date

2024-12-31

Last updated

2025-02-25

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

Conditions

Body Composition, Computed Tomography, Deep Learning, Sarcopenia

Brief summary

To establish an artificial intelligence model for automated diagnosis of sarcopenia based on CT imaging

Detailed description

With the accelerating aging process, the early identification and diagnosis of sarcopenia, along with the effective prevention of its adverse outcomes, have become a focal point in medical research. However, current methods for assessing and diagnosing sarcopenia still face significant limitations, making the development of more efficient and accurate techniques for muscle mass evaluation an urgent clinical need. Although CT is considered as the most promising method for assessing muscle mass, its practical application is hindered by factors such as reliance on physician expertise and time-consuming procedures, limiting its widespread clinical adoption. In light of these challenges, this study aims to develop an artificial intelligence model for fully automated muscle mass measurement based on abdominal CT imaging and to validate its application value in assisting the diagnosis of sarcopenia.

Interventions

None listed

Study design

Observational model

CASE_ONLY

Time perspective

CROSS_SECTIONAL

Eligibility

Sex/Gender

ALL

Age

18 Years to 90 Years

Healthy volunteers

Yes

Inclusion criteria

： 1. The population undergoing BIA and abdominal CT examinations; 2. Can cooperate to complete human body composition analysis, grip strength measurement, 6m walking time measurement, and questionnaire survey.

Exclusion criteria

： 1. Age\<18 years old; 2. Existence of abdominal wall edema; 3. History of spinal surgery or vertebral fractures, or vertebral tumor lesions; 4. History of neuromuscular disorders.

Design outcomes

Primary

Measure	Time frame	Description
To automatedly and precisely quantify three-dimensional muscle volume and fat volume.	2020-2023	To achieve an automated and precise quantification of three-dimensional muscle volume and fat volume at the L3 vertebral region by deep learning.
To establish an artificial intelligence model for diagnosis of sarcopenia.	2020-2023	The validation of artificial intelligence models can assist in the diagnosis of sarcopenia.

Countries

China

Outcome results