Non-Invasive Medical Digital Twins using Physics-Informed
  Self-Supervised Learning
Keying Kuang Frances Dean Jack B. Jedlicki David Ouyang Anthony Philippakis David Sontag Ahmed M. Alaa
A digital twin is a virtual replica of a real-world physical phenomena that
uses mathematical modeling to characterize and simulate its defining features.
By constructing digital twins for disease processes, we can perform in-silico
simulations that mimic patients' health conditions and counterfactual outcomes
under hypothetical interventions in a virtual setting. This eliminates the need
for invasive procedures or uncertain treatment decisions. In this paper, we
propose a method to identify digital twin model parameters using only
noninvasive patient health data. We approach the digital twin modeling as a
composite inverse problem, and observe that its structure resembles pretraining
and finetuning in self-supervised learning (SSL). Leveraging this, we introduce
a physics-informed SSL algorithm that initially pretrains a neural network on
the pretext task of solving the physical model equations. Subsequently, the
model is trained to reconstruct low-dimensional health measurements from
noninvasive modalities while being constrained by the physical equations
learned in pretraining. We apply our method to identify digital twins of
cardiac hemodynamics using noninvasive echocardiogram videos, and demonstrate
its utility in unsupervised disease detection and in-silico clinical trials.