Comparison of Computed Tomography Data With Routine Measurements Concerning Bone and Muscle Health of Aged Individuals

This study focuses on researching sarcopenia and bone loss (osteoporosis), aiming to develop early and effective methods for diagnosis and treatment. These health issues significantly contribute to falls, fractures, and loss of independence and quality of life in old age, particularly affecting individuals impairments. To address these challenges, the study employs innovative imaging techniques based on artificial intelligence (AI) to accurately assess age-related muscle atrophy. A central approach is to analyze existing computed tomography (CT) images of older adults, using retrospective data to evaluate muscle quality. This method aims to efficiently assess muscle quality without additional resources. AI algorithms analyze fine details of muscle tissue, such as muscle adiposity and density. The algorithm can detect fat content within muscles, which negatively impacts muscle health and functionality, and identify irregularities or abnormalities in muscle fibers. This non-invasive approach is crucial for early detection of muscle atrophy and monitoring treatment success. Integrating AI technologies advances beyond conventional imaging techniques, allowing precise analysis of muscle quality. This method not only offers efficient diagnosis and monitoring of sarcopenia but also opens new avenues for personalized therapeutic approaches and improved patient care. Almost every elderly person has at least one existing CT scan, a common and excellent method of medical imaging for significant health issues. These images can be retrospectively analyzed for muscle health. In addition to imaging techniques, the study includes functional tests such as hand strength and walking speed measurements to assess muscle health and condition. These tests establish objective quality characteristics of muscles and assess the effectiveness of prevention and treatment measures. This research aims to provide early diagnosis and effective treatment strategies for sarcopenia and osteoporosis, ultimately improving the quality of life for the elderly. By leveraging AI and existing medical imaging data, the study promotes efficient, sustainable, and precise healthcare solutions for age-related muscle and bone deterioration.

NCT Number	NCT06488872
Lead Sponsor	University Department of Geriatric Medicine FELIX PLATTER
Conditions	Sarcopenia, Muscle Loss, Osteoporosis
Enrollment	300 participants
Start Date	2024-05-01
Primary Completion	2025-09-01 (estimated)
Study Completion	2025-09-01 (estimated)
Updated on ClinicalTrials.gov	2024-07-11