Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure–function relationship in the lung.

In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters $$a_f$$af and $$b_f$$bf associated with fiber direction ( $$R^2_{a_f}=99.471\%$$Raf2=99.471% and $$R^2_{b_f}=92.837\%$$Rbf2=92.837% ). After conducting permutation feature importance analysis, the ML performance further improved for $$b_f$$bf ( $$R^2_{b_f}=96.240\%$$Rbf2=96.240% ), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases.

Background: Global indices of right ventricle (RV) function provide limited insights into mechanisms underlying RV remodeling in pulmonary hypertension (PH). While RV myocardial architectural remodeling has been observed in PH, its effect on RV adaptation is poorly understood. Methods: Hemodynamic assessments were performed in 2 rodent models of PH. RV free wall myoarchitecture was quantified using generalized Q-space imaging and tractography analyses. Computational models were developed to predict RV wall strains. Data from animal studies were analyzed to determine the correlations between hemodynamic measurements, RV strains, and structural measures. Results: In contrast to the PH rats with severe RV maladaptation, PH rats with mild RV maladaptation showed a decrease in helical range of fiber orientation in the RV free wall (139º versus 97º; P =0.029), preserved global circumferential strain, and exhibited less reduction in right ventricular-pulmonary arterial coupling (0.029 versus 0.017 mm/mm Hg; P =0.037). Helical range correlated positively with coupling ( P =0.036) and stroke volume index ( P <0.01). Coupling correlated with global circumferential strain ( P <0.01) and global radial strain ( P <0.01) but not global longitudinal strain. Conclusions: Data analysis suggests that adaptive RV architectural remodeling could improve RV function in PH. Our findings suggest the need to assess RV architecture within routine screenings of PH patients to improve our understanding of its prognostic and therapeutic significance in PH.

Background: Pulmonary hypertension (PH) results in increased right ventricular (RV) afterload, leading to RV dysfunction and failure. The mechanisms underlying maladaptive RV remodeling are poorly understood. In this study, we investigated the multiscale and mechanistic nature of RV free‐wall (RVFW) biomechanical remodeling and its correlations with RV function adaptations. Methods: Mild and severe models of PH, consisting of a hypoxia model in Sprague–Dawley rats (n=6 each, control and PH) and a Sugen‐hypoxia model in Fischer rats (n=6 each, control and PH), were used. Organ‐level function, tissue‐level stiffness, and microstructure were quantified through in vivo and ex vivo measures, respectively. Multiscale analysis was used to determine the association between fiber‐level remodeling, tissue‐level stiffness and anisotropy, and organ‐level dysfunction. Results: Decreased RV–pulmonary artery coupling correlated strongly with RVFW stiffening but showed a weaker association with the loss of RVFW anisotropy. Machine‐learning classification identified the range of adaptive and maladaptive RVFW stiffening. Multiscale modeling revealed that increased collagen fiber tautness was a key remodeling mechanism that differentiated severe from mild stiffening. Myofiber orientation analysis indicated a shift away from the predominantly circumferential fibers observed in healthy RVFW specimens, leading to a significant loss of tissue anisotropy. Conclusions: Multiscale biomechanical analysis indicated that, although hypertrophy and fibrosis occur in both mild and severe PH, certain fiber‐level remodeling events, including increased tautness of collagen fibers and significant reorientations of myofibers, contributed to excessive biomechanical maladaptation of the RVFW leading to severe RV–pulmonary artery uncoupling. Collagen fiber remodeling and the loss of tissue anisotropy can provide an improved understanding of the transition from adaptive to maladaptive RV remodeling.