Comparison of Artificial Intelligence-based Analysis of Computed Tomography Data With Routinely Performed Measurements Concerning Bone and Muscle Health of Aged Individuals to Validate Surrogate Parameters for the Aging Population.
Trial ID: NCT06488872
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-05-01
Completion date: 2025-09-01
Sponsor: University Department of Geriatric Medicine FELIX PLATTER
Conditions: Sarcopenia; Muscle Loss; Osteoporosis
Interventions: comparison of CT scan with routinely assessed methods of muscle mass and strength
Locations: Universitäre Altersmedizin Felix Platter
Countries: Switzerland
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
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.

Eligibility criteria:
Inclusion Criteria:

* CT examination (thorax, abdomen, pelvis, spine with muscle parts to be visualized) by the responsible in-house radiology department one month before or after the inpatient stay at the UAFP (sarcopenia arm).
* CT thorax and CT abdomen images of patients from the responsible in-house radiology department and an in-house DEXA measurement. Both examinations may be performed no more than 18 months apart (osteoporosis arm).
* Diagnostic image quality of CT scans.

Exclusion Criteria:

* Presence of a documented refusal.
* Non-diagnostic image quality
* Absence of the following functional measurements: Hand strength on both hands, timed-up \& go test, gait speed.

Outcomes:
Muscle Volume in mm3 on CT; fat percentage in muscle density (in HU) on CT.; bone density/attenuation in HU on CT.