Artificial Intelligence for Computational Modeling of the Heart
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- Taschenbuch
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 125.90
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
25.11.2019
Herausgeber
Tommaso Mansi + weitereVerlag
Elsevier Science & Techn.Seitenzahl
274 (Printausgabe)
Sprache
Englisch
EAN
9780128168950
Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient's heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications.
- Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications
- Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data
- Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation
- Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications
- Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data
- Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation
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