Upcoming Event: Center for Computational Medicine Seminar

A computational simulation platform for respiratory modeling - validation with experimental data for clinical and pharmaceutical applications

Jonas Biehler, Co-Founder & CTO, Ebenbuild

Abstract

The intrinsic complexity of the respiratory system poses significant challenges in the management and therapy of respiratory diseases, for example, in the tailoring of lung-protective invasive mechanical ventilation of patients suffering from acute respiratory distress syndrome (ARDS), or the targeted delivery of an aerosol to the desired site of action within the lungs. Any development attempting to improve upon the state of the art is not only challenged by the necessity to overcome the intra- and interpatient variability but also by the lack of quantitative, actionable intelligence which current imaging techniques fail to provide.

To overcome these challenges, we introduce a computational whole-lung model which – in combination with machine learning and computer vision – enables us to resolve the internals of the respiratory system in silico at scale, while being able to account for the highly specific physiological and pathophysiological variability of the lung at high spatial resolution. Based on these model capabilities, accurate predictions of the deformation of the of alveolar tissue can provide insight into the cumulative biotrauma resulting from invasive mechanical ventilation and guide therapy decisions. In addition, the resolution of airflow in combination with an efficient approach for the simulation of particle transport and deposition, enables us to predict the fate of inhaled aerosols within the respiratory system with single-particle resolution.

We showcase the accuracy in a clinical setting, and also demonstrate the predictive capabilities for orally inhaled drugs. For patients suffering from ARDS, model-based predictions of regional ventilations are compared against experimental data from electrical impedance tomography. Similarly, we demonstrate the ability to accurately predict particle transport and depositions against 3D SPECT/CT images, resolving the complete airway tree spatially across the entire lung, thereby enabling a previously unprecedented level of detail. 

Biography

Dr. Jonas Biehler is co-founder and CTO of the Munich-based deep tech healthcare startup Ebenbuild, where he is responsible for the company’s product development and R&D.

Ebenbuild develops personalized, digital twins of the lungs. Its digital toolset is based on physics-based simulation, AI, and data science and is designed to support decision-makers in healthcare and life sciences.

Jonas studied Mechanical Engineering at the Technical University of Munich (TUM) and the University of Canterbury in Christchurch, New Zealand. Holding a Ph.D. in computational mechanics from TUM, he has extensive experience in transdisciplinary research projects and entrepreneurial endeavours at the intersection of physics, machine learning, and medicine. Both his research and his entrepreneurial work have been awarded nationally and internationally.