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

NCT06845462 | Completed

• 1 other identifier: LY2023-150-A
• Study Type: observational
• Target: 1,080
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Deep Learning; Sarcopenia; Computed Tomography; Body Composition

Outcome Measures

Primary Outcomes (2)

• 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.

  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.

  2020-2023

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

960 inpatients in the geriatric department of Renji Hospital, 20 patients from Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine, 20 patients from The First Affiliated Hospital of Zhejiang Medical University, 50 patients from The First Affiliated Hospital of Wenzhou Medical University, and 30 patients from Huangshan People's Hospital.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• RenJi Hospital: lead

Study Sites (1)

Shanghai Jiaotong University School of Medicine, Renji Hospital Ethics Committee

Shanghai, Shanghai Municipality, 2000127, China

Location

MeSH Terms

Conditions

Sarcopenia

Condition Hierarchy (Ancestors)

Muscular Atrophy; Neuromuscular Manifestations; Neurologic Manifestations; Nervous System Diseases; Atrophy; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Administrative Director of Geriatrics Department

Study Record Dates

• First Submitted: February 20, 2025
• First Posted: February 25, 2025
• Study Start: September 5, 2023
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2024
• Last Updated: February 25, 2025

Record last verified: 2025-02

Locations

CN (1)