Scaling Digital Heart Twins
PHYSICAL AI FOR CARDIOLOGY - We bypass slow, costly simulations to deliver sub-second cardiac twins directly to the bedside.
Unlocking the data needed to scale digital heart twins—accelerating medical discovery and helping save and improve millions of lives.
Amine Chabane, Founder/CEO
THE CHALLENGE & THE INSIGHT
The Computational Bottleneck
Traditional heart modeling relies on slow, costly, high-fidelity multi-physics simulations. This computational delay prevents digital twins from reaching routine clinical diagnostics, continuous monitoring, and large-scale, in-silico trials
The Core Insight
"Physics-informed AI models are only as powerful as the volume and fidelity of the simulation data used to train them."
Rather than trying to accelerate raw computing hardware, Spikitude solves this bottleneck at the data-generation layer to train robust, clinical-grade neural operator
OUR SYSTEM ARCHITECTURE
From Simulation Data to Bedside Twins
By owning the entire pipeline from high-fidelity data generation to domain-specific neural architectures, we build a highly defensible advantage in speed, precision, and clinical utility
The Foundation
High-Fidelity Data Engine
01
Powering our platform with active synthesis of gold-standard simulation datasets to eliminate the constraint of scarce clinical data
The Core AI
Physics-Informed AI
02
Specialized neural operators and World Models trained on superior data to guarantee absolute physiological consistency
Real-Time Clinical Inference
Scalable Digital Twins
03
Delivering sub-second, patient-specific predictive software directly to the bedside to assist clinicians in instant diagnostics
OUR PHYSICAL AI METHODOLOGY
We turn the living physics of the heart into reliable, real-time intelligence through a rigorous five-stage process.
We translate the complex, coupled partial differential equations of cardiac physics—fluid-structure interaction, tissue mechanics, and electrophysiology—into a structured machine-learning boundary-value framework. Rather than treating the heart as a static image, we model its continuous physical fields across space and time.
This is our primary competitive moat. Because real-world devices cannot capture dense volumetric physics and traditional multi-physics solvers are too slow, our High-Fidelity Data Engine synthetically generates and curates gold-standard, physically consistent training datasets at massive scale.
We design specialized neural architectures capable of representing continuous physical fields. Advanced neural operators (including Fourier Neural Operators) and hybrid neuromorphic World Models instantly map patient-specific boundaries and physiological states into real-time predictive outputs.
Standard statistical losses that only match pixels are insufficient for clinical use. We embed the fundamental laws of physics and biology directly into the objective. The model is mathematically penalized whenever it violates mass conservation, momentum balance, or myocardial membrane potentials—ensuring predictions remain physiologically consistent, clinically safe, and rigorously grounded.
Drawing on our systems and co-design expertise, we select, parallelize, and accelerate the training and inference algorithms. These routines run on high-performance GPU clusters and our dedicated ASICs to deliver our ultimate goal: sub-second, real-time patient twins that support bedside clinical decision-making.
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