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

Shanghai Jiao Tong University

Status: Completed

Conditions: Deep Learning; Computed Tomography; Sarcopenia; Body Composition

Study type: Observational

Funder types: Other

Identifiers

NCT06845462

LY2023-150-A

Details and patient eligibility

About

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

Full 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.

Enrollment: 1,080 patients

Sex: All

Ages: 18 to 90 years old

Volunteers: Accepts Healthy Volunteers

Inclusion and exclusion criteria

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.