IntroductionDigital twins of the respiratory system have shown promise in predicting the patient-specific response of lungs connected to mechanical ventilation. However, modeling the spatiotemporal response of the lung tissue through high-fidelity numerical simulations involves computing times that largely exceed those required in clinical applications. In this work, we present a multi-fidelity deep learning surrogate model to efficiently and accurately predict the poromechanical fields that arise in lungs connected to mechanical ventilation.MethodsWe generate training datasets with two fidelity levels from non-linear finite-element simulations on coarse (low-fidelity) and fine (high-fidelity) discretizations of the lungs domain. Further, we reduce the output spatiotemporal dimensionality using singular value decomposition, capturing over 99% of the variance in both displacement and alveolar pressure fields with only a few principal components. Based on this procedure, we learn both the input-output mappings and fidelity correlations by training a reduced-order multi-fidelity neural network model (rMFNN) that leverages the abundant low-fidelity data to enhance predictions from scarce high-fidelity simulations.ResultsCompared to a reduced-order single-fidelity neural network (rSFNN) surrogate, the rMFNN achieves superior predictive accuracy in predicting spatiotemporal displacement and alveolar pressure fields (R2 ≥ 93% (rMFNN) vs R2 ≥ 75% (rSFNN)). In addition, we show that rMFNN outperforms rSFNN in terms of accuracy for the same level of training cost. Further, the rMFNN model provides inference times of less than a minute, offering speed-ups up to 462× when compared to finite-element numerical simulations.DiscussionThese results demonstrate the potential of the rMFNN lung model to enable patient-specific predictions in acceptable computing times that can be used to personalize mechanical ventilation therapy in critical patients.