LightHearted AI

Captures physiological cardiac signals (likely photoplethysmography, thermal imaging, or radar-based contactless sensing) without physical contact to the patient, applies real-time signal conditioning including noise filtering, artifact removal, and normalization to prepare raw sensor data for downstream AI analysis. The contactless approach eliminates cross-contamination vectors and sterilization overhead while maintaining signal fidelity across diverse patient demographics and environmental conditions.

Applies deep learning models (likely convolutional neural networks or transformer architectures) trained on large cardiac signal datasets to classify presence/absence of heart disease and identify specific pathologies (arrhythmias, structural abnormalities, ischemia indicators) from preprocessed contactless sensor data. The model ingests normalized waveform features and outputs probabilistic disease classifications with confidence scores, enabling rapid triage without cardiologist interpretation.

Synthesizes AI classification outputs into structured clinical reports including disease presence/absence, pathology type, risk stratification, and recommended next steps (e.g., cardiology referral, repeat screening interval). The system likely templates report generation with configurable detail levels for different stakeholders (clinicians vs. patients) and integrates with EHR systems for seamless documentation workflow.

Orchestrates sequential processing of multiple patients through the contactless acquisition → signal preprocessing → AI classification → report generation pipeline, with queue management, priority routing, and progress tracking. The system likely implements asynchronous job scheduling to handle variable acquisition times and computational latency, enabling high-throughput screening workflows in clinic settings.

Stores historical screening results and AI classifications for individual patients, enabling trend analysis across multiple screening sessions to detect disease progression, treatment response, or arrhythmia patterns over time. The system likely implements time-series analytics to identify statistically significant changes in cardiac metrics and flag clinically relevant deterioration requiring intervention.

Exports de-identified screening data (raw signals, AI classifications, patient demographics) in standardized formats (CSV, DICOM, HL7) for integration with research databases and clinical trial platforms. The system implements HIPAA-compliant data anonymization, audit logging, and role-based access controls to enable researchers to analyze screening cohorts while maintaining patient privacy and regulatory compliance.