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

Completed

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

• Registration Number: NCT06845462
• Lead Sponsor: RenJi Hospital

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.

Study Timeline

• Study Start: 2023-09-05
• Primary Completion: 2024-12-31
• Study Completion: 2024-12-31
• Status Verified: 2025-02
• Study First Submitted: 2025-02-20
• Study First Submitted QC: 2025-02-20
• Last Update Submitted: 2025-02-20
• Study First Posted: 2025-03-25
• Last Update Posted: 2025-03-25

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1080

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

Shanghai Jiaotong University School of Medicine, Renji Hospital Ethics Committee; 🇨🇳 Shanghai, Shanghai, China