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

RenJi Hospital1,080 enrolled

Overview

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

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.

Study Type

OBSERVATIONAL

Enrollment

1,080

Conditions

Deep Learning Sarcopenia Computed Tomography Body Composition

Eligibility

Sex: ALLMin age: 18 YearsMax age: 90 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (1)

Shanghai Jiaotong University School of Medicine, Renji Hospital Ethics Committee

Shanghai, Shanghai Municipality, China

Outcomes

Primary Outcomes

To automatedly and precisely quantify three-dimensional muscle volume and fat volume.

To achieve an automated and precise quantification of three-dimensional muscle volume and fat volume at the L3 vertebral region by deep learning.

Time frame: 2020-2023

To establish an artificial intelligence model for diagnosis of sarcopenia.

The validation of artificial intelligence models can assist in the diagnosis of sarcopenia.

Time frame: 2020-2023

Data from ClinicalTrials.gov

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