ResearchHealthcare

AI frameworks for cardiovascular and stroke risk in patients with comorbidities

A body of research on how AI can estimate cardiovascular and stroke risk in people with diabetes, Parkinson’s disease, rheumatoid arthritis, sleep apnoea and COVID-19.

The challenge

Patients with chronic conditions often carry hidden cardiovascular risk that conventional risk calculators underestimate. Clinicians need tools that combine imaging such as carotid ultrasound with clinical risk factors, and that explain their reasoning.

Our approach

  1. Study the links between each comorbidity and cardiovascular disease.
  2. Design AI frameworks that combine imaging-based markers with conventional risk factors.
  3. Address explainability and bias so results can be trusted across patient groups.

The solution

  • Risk stratification frameworks per comorbidity
  • Explainable AI design
  • Bias assessment approach

Results

  • More than ten papers in international journals, including Diagnostics, Journal of Clinical Medicine, Metabolites and Reviews in Cardiovascular Medicine.
  • The foundation of BigHealth’s risk-scoring module.

Publication

For example: Maindarkar, Khanna, Puvvula et al. (2024). Reviews in Cardiovascular Medicine, 25(12), 463. View source

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