AI Detects Hidden Fall Risks in Middle-Aged Adults Years Earlier

A new study suggests AI could help spot adults at higher risk of falling much earlier than before. Researchers from the Mayo Clinic used artificial intelligence to analyse abdominal CT scans, identifying key markers linked to future falls. The findings highlight that warning signs may appear as early as middle age—long before problems start.

Falls remain the top cause of injury for adults over 65, with around one in four older people affected each year. In the UK alone, nearly 1 million hospital visits and 319,000 hip fractures result from falls annually. But the latest research shows that risk factors may emerge far sooner than previously thought.

The Mayo Clinic team trained AI algorithms to assess muscle, fat, and bone quality in abdominal CT scans. Their analysis revealed that muscle density—a measure of muscle health—was a far stronger predictor of fall risk than muscle size alone. This link was particularly clear in people aged 45 to 64, suggesting that core strength in middle age plays a critical role in long-term stability.

While studies on sarcopenia and AI-assisted imaging exist, few clinics currently use these methods for fall prevention in younger adults. As of early 2026, no standard guidelines integrate CT-based muscle analysis into routine care for this age group. The researchers argue that early detection could pave the way for targeted interventions before balance problems develop.

The study points to a potential shift in how doctors assess fall risk. By monitoring muscle density in middle-aged adults, healthcare providers might intervene sooner to strengthen core muscles and prevent future injuries. However, wider adoption of these AI-driven techniques will depend on further research and updated clinical guidelines.