Integration of Bioimpedance Monitoring and Artificial Intelligence for Early Diagnosis of Predictors of the Cardiovascular Continuum and Oncological Risks

This pilot study explores the integration of bioimpedance monitoring (BIM) and artificial intelligence (AI) for early detection of subclinical cardiovascular and oncological risks. Using data collected from patients with hypertension, we analyzed key bioimpedance parameters before and after treatment with the NICaS system. AI algorithms were applied to predict hidden disease risks based on hemodynamic and fluid balance indicators. The approach demonstrated high sensitivity in identifying early predictors of chronic conditions. This research proposes a novel AI+BIM-based method for preventive diagnostics and personalized medicine.