Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.
Introduction: Computational modeling-guided, personalized electrophysiology (EP) intervention for atrial fibrillation (AF) is an emerging paradigm of precision medicine. In the published models, advanced imaging and invasive mapping achieve personalization of cardiac anatomy. However, EP cellular personalization is less developed, and parameters are often assumed to be uniform across individual patients Hypothesis: Anatomical personalization alone is not sufficient to recapitulate the individual clinical features of AF in personalized models. Methods: For each of 57 patients (66±10 yr, 30 persistent) referred for catheter ablation of AF, we constructed a personalized 3-D model using pre-ablation CT, invasive mapping, and Courtemanche-Ramirez-Nattel atrial cell model. In the pre-ablation model, we performed virtual ramp pacing with cycle lengths at 200-120ms within the coronary sinus to induce AF. AF was defined as being inducible when it persists for >15s. Simulation was repeated for 5 different published sets of EP parameters of ionic currents. In total, >10k simulations were performed on an NVIDIA V100 GPU cluster, using the Lattice Boltzmann method with spatial resolution of 0.5mm and temporal resolution of 0.05ms, achieving a runtime of 24s per 1s of simulation ( Fig. A ). Results: While 100% AF inducibility was expected in the personalized pre-ablation models, AF inducibility varied between 27% and 96% depending on the set of ionic parameters used ( Fig. B ). The large variability of AF inducibility indicates that those models are highly sensitive to EP parameters, therefore anatomical personalization alone cannot adequately constrain the individual features of AF. Conclusions: The patient-specificity of the current paradigm of modeling-guided, personalized EP intervention is limited. There is an unmet clinical need for incorporation of personalized EP as well as anatomical parameters to achieve true precision medicine.
Kurzfassung Eine zielgerichtete Verbesserung von Produktions- und Logistiksystemen in Richtung der sogenannten Smarten Fabrik erfordert zunächst eine strukturierte Erfassung, Bewertung und Darstellung des Ist-Zustands hinsichtlich Lean und Industrie 4.0. Vor diesem Hintergrund wurde das Smart Factory Assessment (SFA) entwickelt, an zahlreichen globalen Fertigungsstandorten angewandt und validiert. Im vorliegenden Beitrag werden die Methodik, Auszüge eines Praxisbeispiels sowie erzielte Erkenntnisse vorgestellt und reflektiert. Das aus zwölf Kategorien bestehende Experten-Assessments ermöglicht die Definition konkreter Verbesserungspfade in Richtung Lean- und Industrie-4.0-Exzellenz – ein Wegbereiter für die Smarte Fabrik.
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