arXiv subjects

Dirk Schädler

Publications and source records attributed to Dirk Schädler.

3 recordsLinked to original sources

arXiv2026

Validation of a Computational Respiratory System Model for Mechanical Ventilation

Computational modeling and simulation have emerged as powerful tools for assessing medical device performance and safety, particularly in silico clinical trials (ISCTs) for automated medical systems. In ventilation, where gas exchange, respiratory mechanics, and patient-ventilator interaction must be managed under evolving pathophysiology, clinical translation of automated control strategies remains slow and resource-intensive. These challenges are particularly relevant for AI-based therapy-control systems, whose data-driven decision-making must be evaluated across heterogeneous and safety-critical patient states that may be sparsely represented in clinical datasets. Mechanistic, physiology-based models provide a complementary and interpretable environment for testing such scenarios. This paper applies a standards-aligned framework for credibility assessment of a computational respiratory model, demonstrated using an automated weaning case study. The framework operationalizes ASME V&V 40 and FDA principles within a structured validation workflow. The model integrates respiratory mechanics, gas exchange, respiratory control, and a ventilator representation, with validation under a defined context of use and explicit questions of interest. Model credibility is assessed through calibration, physiological plausibility, population-based evaluation, and reproduction of emergent behavior. All model requirements derived from the intended context of use are addressed, and gaps are transparently reported. The resulting credibility argument supports applicability of the model for medium-low-risk preclinical ISCTs of automated weaning strategies. Residual limitations relate to the extent of in vivo evidence, population representativeness, and external validation. The validation procedure provides a blueprint for validation of this and similar models in mechanical ventilation and related use cases.

physics.med-ph↗

arXiv2025

Patient-specific prediction of regional lung mechanics in ARDS patients with physics-based models: a validation study

The choice of lung protective ventilation settings for mechanical ventilation has a considerable impact on patient outcome, yet identifying optimal ventilatory settings for individual patients remains highly challenging due to the inherent inter- and intra-patient pathophysiological variability. In this validation study, we demonstrate that physics-based computational lung models tailored to individual patients can resolve this variability, allowing us to predict the otherwise unknown local state of the pathologically affected lung during mechanical ventilation. For seven ARDS patients undergoing invasive mechanical ventilation, physics-based, patient-specific lung models were created using chest CT scans and ventilatory data. By numerically resolving the interaction of the pathological lung with the airway pressure and flow imparted by the ventilator, we predict the time-dependent and heterogeneous local state of the lung for each patient and compare it against the regional ventilation obtained from bedside monitoring using Electrical Impedance Tomography. Excellent agreement between numerical simulations and experimental data was obtained, with the model-predicted anteroposterior ventilation profile achieving a Pearson correlation of 96% with the clinical reference data. Even when considering the regional ventilation within the entire transverse chest cross-section and across the entire dynamic ventilation range, an average correlation of more than 81% and an average root mean square error of less than 15% were achieved. The results of this first systematic validation study demonstrate the ability of computational models to provide clinically relevant information and thereby open the door for a truly patient-specific choice of ventilator settings on the basis of both individual anatomy and pathophysiology.

physics.med-ph↗

arXiv2023

Pressure- and time-dependent alveolar recruitment/derecruitment in a spatially resolved patient-specific computational model for injured human lungs

We present a novel computational model for the dynamics of alveolar recruitment/derecruitment (RD), which reproduces the underlying characteristics typically observed in injured lungs. The basic idea is a pressure- and time-dependent variation of the stress-free reference volume in reduced dimensional viscoelastic elements representing the acinar tissue. We choose a variable reference volume triggered by critical opening and closing pressures in a time-dependent manner from a straightforward mechanical point of view. In the case of (partially and progressively) collapsing alveolar structures, the volume available for expansion during breathing reduces and vice versa, eventually enabling consideration of alveolar collapse and reopening in our model. We further introduce a method for patient-specific determination of the underlying critical parameters of the new alveolar RD dynamics when integrated into the tissue elements, referred to as terminal units, of a spatially resolved physics-based lung model that simulates the human respiratory system in an anatomically correct manner. Relevant patient-specific parameters of the terminal units are herein determined based on medical image data and the macromechanical behavior of the lung during artificial ventilation. We test the whole modeling approach for a real-life scenario by applying it to the clinical data of a mechanically ventilated patient. The generated lung model is capable of reproducing clinical measurements such as tidal volume and pleural pressure during various ventilation maneuvers. We conclude that this new model is an important step toward personalized treatment of ARDS patients by considering potentially harmful mechanisms - such as cyclic RD and overdistension - and might help in the development of relevant protective ventilation strategies to reduce ventilator-induced lung injury (VILI).

physics.med-ph↗