Personalized digital twins forecast lung fate during ex vivo perfusion

Researchers analyzed data from nearly 1,000 EVLP procedures to train a hybrid physics-ML model that creates a dynamic digital twin of a lung, enabling time-resolved predictions of 75+ functional and molecular parameters and early updates as new data arrive. The twin serves as a personalized control to predict no-treatment trajectories, allowing comparison with actual therapy (e.g., alteplase) to assess responses while reducing reliance on matched organs and mitigating inter-organ variability in preclinical transplantation studies.