title: A self-taught artificial agent for multi-physics computational model personalization.
abstract: Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.
authors.affiliation: Medical Imaging Technologies, Siemens Healthcare GmbH, Erlangen, Germany; Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany. Electronic address: dominik.neumann@siemens.com.
authors.forename: Dominik
authors.initials: D
authors.lastname: Neumann
authors.affiliation: Medical Imaging Technologies, Siemens Healthcare, Princeton, USA.
authors.forename: Tommaso
authors.initials: T
authors.lastname: Mansi
authors.affiliation: Siemens Corporate Technology, Siemens SRL, Brasov, Romania; Transilvania University of Brasov, Brasov, Romania.
authors.forename: Lucian
authors.initials: L
authors.lastname: Itu
authors.affiliation: Medical Imaging Technologies, Siemens Healthcare, Princeton, USA.
authors.forename: Bogdan
authors.initials: B
authors.lastname: Georgescu
authors.affiliation: Department of Internal Medicine III, University Hospital Heidelberg, Germany.
authors.forename: Elham
authors.initials: E
authors.lastname: Kayvanpour
authors.affiliation: Department of Internal Medicine III, University Hospital Heidelberg, Germany.
authors.forename: Farbod
authors.initials: F
authors.lastname: Sedaghat-Hamedani
authors.affiliation: Department of Internal Medicine III, University Hospital Heidelberg, Germany.
authors.forename: Ali
authors.initials: A
authors.lastname: Amr
authors.affiliation: Department of Internal Medicine III, University Hospital Heidelberg, Germany.
authors.forename: Jan
authors.initials: J
authors.lastname: Haas
authors.affiliation: Department of Internal Medicine III, University Hospital Heidelberg, Germany.
authors.forename: Hugo
authors.initials: H
authors.lastname: Katus
authors.affiliation: Department of Internal Medicine III, University Hospital Heidelberg, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Meder
authors.affiliation: Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Steidl
authors.affiliation: Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany.
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.affiliation: Medical Imaging Technologies, Siemens Healthcare, Princeton, USA.
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D010825:Physics; D057285:Precision Medicine / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Artificial intelligence; Computational modeling; Model personalization; Reinforcement learning