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Mechanical ventilation (MV) plays a vital role in intensive care by ensuring adequate gas exchange in patients who are unable to breathe independently, such as those with acute respiratory distress syndrome (ARDS). However, a major challenge associated with MV is ventilator-induced lung injury (VILI), which remains a frequent and serious complication. Two key types of VILI are volutrauma and atelectrauma, arising due to alveolar overdistension and cyclic collapse and reopening, respectively. To minimize the risk of VILI, ventilator settings, such as positive end-expiratory pressure (PEEP), must be carefully optimized for each individual patient. Despite this need for personalization, current bedside techniques lack the ability to continuously measure local alveolar dynamics. As a result, clinicians must rely on ventilator-derived global metrics, such as pressure-volume relationships and overall compliance, which may not fully capture the complexities of regional lung behavior.

Computational modeling offers a promising solution by linking local alveolar mechanics with global lung function. Such models have the potential to support clinicians in delivering more precise, patient-specific ventilation strategies, ultimately improving outcomes and reducing the risk of VILI.

Therefore, the aim of this research is to develop and apply digital twin technologies to create computational models of the human respiratory system. An overview of the general modeling workflow employed in this research is illustrated below. These models will then be applied to clinically relevant use cases related to MV to advance understanding and aid clinical decision-making in the management of respiratory diseases through patient-specific insights.

R. Dunphy, S. Quicken, J. van Kimmenade, J. Mangold, M. Shekarnabi, V. Estopier Castillo, M. Orkisz, A. De Bie Dekker, I. Paulussen, J. Richard, W. Huberts, S. Bayat. Patient-specific lung simulation incorporating regional elastance and recruitment to guide mechanical ventilation in acute respiratory distress syndrome. 2026. Journal of Applied Physiology, 140:5, 1139-1151, 10.1152/japplphysiol.01215.2025.