Metra Biomedical

Metra Biomedical develops predictive models that are physiologically credible and operationally deployable. Our work spans high-frequency waveform analysis, multimodal clinical prediction, and latent-state estimation. Where governing equations are incomplete or unavailable, we apply sparse system identification and symbolic model distillation to recover compact, interpretable relationships from complex real-world datasets.

• Custom predictive models for waveform-based diagnostics, multimodal clinical data, and longitudinal monitoring
• Hybrid and physics-informed modeling for inverse problems and latent physiological state estimation
• Data-driven discovery of governing equations and compact interpretable relationships from complex datasets
• Sparse identification of dynamical systems and symbolic model distillation for explainable diagnostics
• Risk prediction, patient stratification, and threshold design for clinical decision support
• Real-time inference strategies for embedded, bedside, and remote-monitoring use cases

We transform unstructured physiological and imaging data into structured analytical representations that support clinical diagnosis, trial screening, and pre-procedural planning. Deliverables range from automated imaging triage and precise anatomical measurements to patient-specific three-dimensional models and virtual device fit assessments.

• ECG, PPG, pressure, and hemodynamic waveform processing: beat detection, morphology analysis, spectral features, drift monitoring, and signal-quality gating
• Automated triage of imaging studies to identify high-yield candidates and flag anatomy or physiology that meets study-specific thresholds
• Conversion of routine 2D or volumetric scans into patient-specific three-dimensional anatomical models and digital twins
• Quantitative extraction of perimeter, area, annular geometry, and other procedural measurements from imaging data
• Virtual pre-procedural fit assessment to evaluate device interaction, anatomical interference, sealing behavior, or likely screen-fail scenarios
• Integrated computational workflows that connect raw imaging, anatomical modeling, simulation, and quantitative review in a reproducible pipeline

Analytical rigor has limited value unless it is communicated in a form that clinicians, engineers, and sponsor teams can act upon. We design interpretable reporting frameworks, eligibility dashboards, and standardized scoring tools that bridge the gap between algorithmic output and clinical practice.

• Standardized candidate-screening workflows for clinical studies, registries, and device evaluations
• Unified eligibility reporting with intuitive decision categories, confidence indicators, and threshold-based flags
• Translation of model outputs into clinically meaningful alerts, review queues, and screening summaries
• Longitudinal trend analysis to support study execution, patient monitoring, and endpoint development
• Dashboard and visualization design for investigators, core labs, and sponsor-facing technical review
• Validation planning, error analysis, subgroup analysis, and technical evidence packages for high-stakes use cases