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.

Age-related muscle wasting and bone loss are significant public health challenges impacting elderly mobility and independence. Sarcopenia, a decline in muscle strength and mass, heightens fall risk, particularly in individuals with dementia or cognitive impairments. This leads to complications, reduced independence, and diminished quality of life. Osteoporosis increases the risk of fractures from falls or spontaneously, necessitating early diagnosis and prevention.

Despite effective treatments for sarcopenia, such as a protein-rich diet and strength training, the condition remains underrecognized due to diagnostic challenges. Common methods like hand strength measurement are problematic for those with rheumatic conditions or Parkinson's, often yielding inaccurate results. Other methods, like measuring leg strength and walking speed, require coordination and balance, which can be difficult for those with dementia or visual impairments. There is a need for tailored diagnostic solutions for diverse aging populations.

Bone mineral density (BMD) is crucial for assessing health in older adults, as low BMD indicates osteoporosis and a higher fracture risk. Traditionally measured by dual X-ray absorptiometry (DEXA), BMD assessment can be difficult for patients with mobility issues, pressure ulcers, or dementia due to the requirement to remain still for extended periods.

New AI-based algorithms can now automatically evaluate body tissues and patterns from routine CT scans, offering reproducible results beyond human capability. AI can quantify muscle mass at specific body cross-sections, such as lumbar vertebral point 3 (L3), which correlates with total body muscle mass and predicts muscle health. CT measurements of thigh and psoas muscles can also indicate whole body skeletal muscle mass. European guidelines highlight the need for muscle quantification in early sarcopenia diagnosis.

Correlating AI-measured muscle mass with functional muscle strength assessments can help identify surrogate parameters for early sarcopenia detection. Additionally, measuring muscle fat content, which correlates with strength loss, is essential for assessing muscle health. Innovative approaches are required, as sarcopenia diagnosis is still evolving and geriatric diseases often need proportionate diagnostic and treatment strategies due to multiple comorbidities.