Artificial Intelligence & Deep Learning
HRV-to-Biochemistry Estimation
&
Translational Decision Support
Translating wearable signals into clinically interpretable, longitudinal decision support.
Scientific Orientation
Dr. Polisetty’s AI program bridges classical physiological reasoning with modern analytics. Systems emphasize interpretability, longitudinal safety, and reproducible validation across time.
Proprietary Algorithms (IP-Protected)
Seventeen proprietary ML algorithms transform HRV-derived inputs into rough estimates of blood biochemistry and physiological parameters.
Active IP protection — architectural details, datasets, and validation reports shared under NDA collaboration.
What the AI Enables
• Docture Poly is a non-invasive, AI-assisted physiological signal analysis platform designed to support health assessment, wellness optimization, and translational research, operating as an adjunctive decision-support system and not as a standalone diagnostic device.
• Advanced Signal Stratification The platform employs AI-assisted signal processing to stratify physiological signals across multiple frequency domains, exceeding conventional low-band models, to enable finer characterization of biological patterns.
• Organ-Oriented Functional Mapping Using computational pattern recognition, the system supports organ-oriented signal mapping intended to provide functional insights into organ-level physiological states rather than diagnostic conclusions.
• Computational Biochemical Correlation Models Advanced mathematical algorithms explore correlative relationships between physiological signals and metabolic markers (e.g., glucose trends, glycemic control indices, lipid patterns). These outputs are estimates for wellness tracking and research correlation only, not replacements for laboratory testing.
• Personalized Wellness Guidance Support Based on aggregated signal patterns, the AI assists in generating personalized, non-prescriptive wellness recommendations, including physical activity guidance, nutritional planning, supplementation strategies, and traditional lifestyle practices.
• Neuro-Humoral Pattern Analysis The platform supports analysis of autonomic and neuro-humoral signal trends associated with stress adaptation, recovery, and physiological regulation.
• Homeostatic Trend Monitoring AI models assist in tracking organ-wise homeostatic trends over time, enabling longitudinal observation of physiological balance and variability.
• Systems Biology Perspective By integrating multi-organ signals, inferred metabolic trends, and regulatory patterns, Docture Poly supports a systems-biology framework for understanding complex human physiology.
• Research & Translational Utility The platform is intended for research, observational studies, and integrative health programs, supporting hypothesis generation, outcome tracking, and translational exploration
Where This Is Headed
Integrated learning health stack: devices → AI translation → protocol automation → clinician dashboards → outcomes analytics — supporting prevention, chronic optimization, and integrative end-stage care.