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AI & Machine Learning · Prototype · 2026

Heart

Real CT-derived four-chamber anatomy inside an MRI-segmented body, driven by a physiological activation model and rendered live in the browser with a synchronized, model-derived 12-lead ECG.

Year
2026
Status
Prototype
Category
AI & Machine Learning
Role
Architect & Lead

Key metrics

162k
Mesh points
12
ECG leads
2.0 mm RMS
Registration
v2.0
Release

Architecture

A 162k-point tetrahedral CT mesh is registered into the BodyParts3D whole body by similarity ICP (2.0 mm RMS). Activation runs as an anisotropic graph-eikonal with a His–Purkinje layer; an equivalent-double-layer forward model produces the 12-lead ECG; a server-side Lambda localizes damage from the ECG alone.

Case study

Heart

A causal model of the human heart you can hold in your hand: real anatomy, real physiology and a real 12-lead ECG, running live in the browser at heart.macleodlabs.com.

The model

  • Anatomy. A 162k-point four-chamber CT mesh with rule-based fibres, registered into an MRI-segmented whole body (2.0 mm RMS), so the heart's position is measured, not staged.
  • Activation. An anisotropic graph-eikonal model with a conduction system: SA node, atria, AV delay, His–Purkinje fast layer, then myocardium.
  • Repolarization. APD90 fields with restitution coupling and apicobasal and transmural gradients, giving concordant T waves.
  • 12-lead ECG. An equivalent-double-layer forward model: 10 physical electrodes, 12 derived leads, one global gain calibrated on normal sinus rhythm.
  • Hemodynamics and motion. A four-chamber 0D circulation drives the PV loop and the heart's motion.

Damage from the ECG alone

Choose a damage scenario, or read a photographed ECG. A server-side inverse model matches the 12-lead trace against a library of simulated lesions and highlights where the damage most likely is.

Not a medical device and not medical advice. Simulation outputs are illustrative, research-grade estimates.

Tech stack

Three.jsPythonEikonal activationEDL forward ECGAWS Lambda

Gallery

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