nmMED · Neuromorphic Medical Software
SLNN is a lightweight Spiking Liquid Neural Network that learns continuously from real-time physiological signals: surgical tremor, patient vitals, EEG, EMG, cardiac pacing, anesthesia depth, and motor rehabilitation effort.
For clinicians, nmMED offers earlier risk detection and precise real-time control. For patients, it delivers highly personalized, adaptive care that responds directly to their physiology, eliminating rigid rule-based constraints.
Core Technology
Instead of merely classifying static data snapshots, SLNN continuously monitors dynamic signal streams. It learns patient-specific baseline behavior and adapts its response in real time to fluctuations in patient physiology, clinician interaction, or hardware performance.
Key Differentiators
Conventional systems rely on static threshold alerts, manually tuned PID loops, and offline trained models. nmMED is custom-engineered for 'moving baselines'—adapting second-by-second to the unique, changing dynamics of a specific patient, clinician, and device.
Seven medical wedges
These are aggressive product directions, not clinical approval claims. The point is clear: medical software can move from rigid thresholds to adaptive physiology.
A live demo compares SLNN against BMFLC+PID under the same tremor input. This is the clearest front door: show surgeons and VCs the movement, not a slide.
Patient upside: steadier instrument behavior and a path toward safer microsurgical assistance.
Fixed alarms treat a patient like a rule table. SLNN can learn that patient baseline and detect when the pattern starts to bend before a dramatic threshold event.
Patient upside: fewer ignored alarms and earlier escalation when deterioration starts.
Rate-responsive pacing still leans on simple proxies. SLNN can read rhythm variability and autonomic-like patterns to suggest smarter patient-specific pacing behavior.
Patient upside: pacing that may feel closer to real physiologic demand.
IONM is noisy: cautery, positioning, baseline drift, and real nerve risk all collide. SLNN can learn the case baseline and rank changes worth expert attention.
Patient upside: fewer false distractions and sharper attention to possible nerve injury.
Anesthesia response is delayed and personal. SLNN can follow BIS, EEG shape, blood pressure, and heart-rate movement as one adaptive state.
Patient upside: smoother supervision of depth, less overshoot, and earlier warning before instability.
Retina work lives in microns. SLNN can combine hand tremor, tool-tip motion, and eye-motion context to help the instrument stay inside a tighter target zone.
Patient upside: a path toward safer training and eventually steadier robot-assisted retina work.
Rehab is repetition, fatigue, effort, and timing. SLNN can align EMG intent with FES or robotic help, then adapt as the patient improves or tires.
Patient upside: more useful repetitions and stimulation timing that follows the nervous system.
Commercial path