Expert / Gen1 baseline: 74% accuracy.
Medical devices
FlowSense Clinical / ACE
Clinical-grade wearable system for noninvasive assessment of CSF shunt patency using thermal transport, with algorithm validation and FDA-ready workflows.
Selected facts
Quantitative details and source-backed proof points.
FlowSense Gen2 blinded clinical validation: 0.81 AUC / 82% accuracy.
Final combination with imaging: 92% sensitivity / 97% NPV.
Biohub deck clinical-study reference: 9 hospitals, 182 subjects, and a 112-subject validation set.
Biohub deck performance reference: 88.9% sensitivity and 91.0% accuracy with CT/MRI.
Supported 3,000+ hours of physiological/device data and 200+ HA Connect datasets across Rhaeos programs.
Project summary
Why it exists, what I built, and what I learned.
Why I built it
I wanted a practical system for turning noisy physiological data into a validated, clinically useful shunt-monitoring workflow.
What I built
Clinical and home-use sensing workflows, model-training pipelines, feature engineering, validation tooling, and FDA-support documentation.
What worked
The strongest part was connecting thermal sensor physics, model validation, and real-world patient data into one workflow.
What failed
Small and biased datasets required careful grouping, leakage control, and feature selection to avoid overfitting.
What I learned
Algorithm work is strongest when it is grounded in the sensing physics and the clinical workflow, not just model metrics.
Stack
Tools, systems, and technical areas involved.
Links and direction
Public links and next steps.
Carry the same validation pattern into adjacent hydrocephalus and wearable-monitoring problems.
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