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Für optimale Therapierfolge muss deren Wirksamkeit aber stetig verbessert werden, indem diese präziser an das Krankheitsbild eines jeweiligen Patienten angepasst werden. Die große Herausforderung bei der Umsetzung von solchen personalisierten Präzisionstherapien besteht darin diese Geräte optimal maßgeschneidert zu implantieren und einzustellen. Eine vielversprechende Möglichkeit dafür bieten virtuelle Herztechnologien, die Anatomie und elektrisches Verhalten eines Patientenherzens physikalisch detailliert simulieren können. Derartige aus Patientendaten konstruierte Computermodelle, die elektrische Vorgänge im Herzen eines Patienten exakt nachbilden können, werden als digitale Zwillinge bezeichnet. Um diese effektiv für die Optimierung von Therapien einsetzen zu können müssen aber bestehende Probleme in der Kalibrierung mit Patientendaten gelöst werden, und die Zuverlässigkeit von Vorhersagen muss nachgewiesen werden.\r\n\r\nDas Forschungsziel besteht darin, die Grundlagen für eine Virtuelle-Herz-Technologie zu schaffen, die in der Lage ist aus klinischen nicht-invasiven Bild- und Messdaten automatisiert mechanistische digitale Zwillingsmodelle zu erstellen, die das elektrische Verhalten der Ventrikel (Hauptkammern) genau abbilden. Diese Herzzwillinge werden mit einem personalisierten Modell des ventrikulären Reizleitungssystems ausgestattet, das es ermöglicht das Elektrokardiogramm (EKG) des normalen Herzschlags zu simulieren und auch das EKG bei Schrittmachertherapien vorherzusagen.\r\n\r\nDazu werden automatisierte Arbeitsabläufe zur Erzeugung von anatomischen Zwillingsmodellen aus Bilddaten, ein echtzeit-fähiges biophysikalisch detailliertes EKG-Modell, Methoden zur Quantifizierung des Einflusses von Beobachtungsunsicherheiten und Techniken zur Identifizierung von Modellparametern entwickelt. Diese Technologien werden zur Erstellung und Kalibrierung von digitalen Zwillingen eingesetzt, die drei verschiedene Patientengruppen repräsentieren – gesunde Probanden, Patienten, die unter infarktbedingten ventrikulären Tachykardien leiden und mittels Ablation therapiert werden, und Patienten mit Reizleitungsstörungen, die mittels Re-synchronisationstherapie behandelt werden. Die dadurch gebildeten virtuellen Kohorten werden validiert, und deren Vorhersagekraft evaluiert. Die Methoden zur Quantifizierung werden eine Abschätzung ermöglichen, mit welcher Wahrscheinlichkeit den Modellvorhersagen vertraut werden kann.\r\n\r\nDie Hauptinnovation des Projekts besteht in der Entwicklung eines systematischen und automatisierten Ansatzes zur Erzeugung von elektrischen Herzmodellen aus klinischen Daten. Die Leistungsfähigkeit der entwickelten Technologie hinsichtlich Personalisierung der elektrischen Herzfunktion und der Vorhersage therapeutischer Effekte soll demonstriert werden. In Kombination mit einer Abschätzung der Vorhersageunsicherheiten soll die Glaubwürdigkeit von digitalen Herzzwillingen nachgewiesen werden, um damit die Grundlage für den Einsatz von simulations-basierten digitalen Zwillingen als wichtiges Werkzeug der Präzisionskardiologie zu legen. Diese Technologien werden auch eine Schlüsselrolle in der zukünftigen Entwicklung medizinischer Geräte in der Industrie spielen, da die Notwendigkeit von Tierexperimenten und klinischen Studien reduziert wird, und somit Therapien wesentlich schneller und kostengünstiger entwickelt werden können.\r\n","en":"Cardiovascular diseases are often treated with implantable medical devices such as pacemakers or defibrillators. For optimal therapeutic success with these devices, it's essential to customize their implantation and settings to provide the best treatment outcome for a specific patient. A promising approach to achieve these personalized precision therapies is through the use of virtual heart technologies (VHTs). These are computer models that can simulate the anatomy and electrical behavior of the heart in great detail. When VHTs are personalized and constructed using data from an individual, referred to as a \"digital twin,\" they can accurately replicate the electrical events in that patient's heart. However, to effectively utilize VHTs for therapy optimization in cardiovascular diseases, we must address existing calibration issues with patient data and ensure the reliability of predictions.\r\n\r\nOur research goal is to establish a workflow to automatically create mechanistic digital twins using non-invasive clinical image and measurement data. These twins would accurately represent the electrical behavior of the ventricles, the main chambers of the heart. The digital twins will be equipped with a personalized model of the ventricular conduction system, allowing them to replicate a normal heartbeat and therefore be specifically helpful for pacemaker therapies.\r\n\r\nFor this purpose, we aim to develop automated workflows for VHTs in the form of digital twins from clinical data. The workflows include a real-time capable biophysically detailed ECG model, methods to quantify modeling uncertainties, and techniques to identify patient-specific model parameters. Resultant VHTs will be used to create and calibrate digital twins representing three different patient groups: healthy subjects, patients suffering from infarct-related ventricular tachycardias who are treated with ablation, and patients with conduction disorders treated with resynchronization therapy. We will validate these virtual cohorts and evaluate their ability to optimize therapeutic outcomes. Our quantification methods will estimate the probability with which treatment predictions from the VHTs can be trusted.\r\n\r\nThe main innovation of this project is the novel workflow for generating personalized VHTs from clinical data. We aim to demonstrate the effectiveness of the digital twins in personalizing the electrical function of the heart and predicting therapeutic effects. We also intend to prove the credibility of these VHTs, laying the groundwork for simulation-based digital twins as crucial tools in precision cardiology. These technologies are expected to play a significant role in the future development of medical devices.\r\n"},"begin_planned":"2022-06-01T02:00:00+02:00","begin_effective":"2022-06-01T02:00:00+02:00","end_planned":"2025-05-31T02:00:00+02:00","end_effective":"2027-03-14T01:00:00+01:00","assignment":"2023-08-31T16:07:34+02:00","program":111,"subprogram":null,"organization":14011,"category":10,"type":10,"partner_function":3,"manager":50966,"contact":null,"status":2,"research":1,"grant":10,"event":null,"study":null,"language":null,"funders":[9],"funder_projectcode":"I 6540","ethics_committee":null,"edudract_number":null,"persons":["7740-50966-10","7740-96212-12","7740-98304-12","7740-50967-12"]},{"id":8952,"title":{"de":"Partikeltherapie bei PatientInnen mit Sarkomen. 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