Computational wound simulation for clinical care

Tissue maps from dressing-change stills

Reading bed composition from clinic photos so serial DFU visits stay comparable under usual care.

Graft-readiness as a research question

Combining image signals with clinic context so a clinician can refuse or advance reconstruction with clearer grounds.

Burn beds that change by the hour

Tracking depth and readiness under one wound ID when the ward cannot wait on a single static still.

Muscle disease as a simulation lane

Exploring MD and autoimmune muscle from records on file - before any dosing conversation starts.

Stall and recovery across visits

Following the same wound over time to surface when healing slows, without claiming a take or heal.

Tissue vision on the desk

Small vision models that map what the camera sees into tissue structure a clinician can critique.

Printability from process tables

Learning which ink and process choices stay printable - inside ranges science already signed.

Scaffold geometry from constraints

Drafting printable structure from clinical and mechanical bounds, handed off as files - not machine control.

Recipe composition under guardrails

Ranking constrained blends with plain-language limits so the lab never invents outside the formulary.

An agent over staged models

One chat that runs classifiers and simulators in sequence - you stay in charge of what gets used.