Condition: Sarcopenia · Muscle Loss · Osteoporosis · Sponsor: University Department of Geriatric Medicine FELIX PLATTER
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 sig…
This description comes directly from the study's public registry record.
Andreas M. Fischer, PD Dr. · 061 3265102 · andreasmfischer.basel@gmail.com
Natalie N Godau, Dr. · 061 3265102 · andreasmfischer.basel@gmail.com
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| Universitäre Altersmedizin Felix Platter | Basel, Canton of Basel-City, Switzerland | Recruiting |
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Source record: clinicaltrials.gov/study/NCT06488872