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UID:submissions.pasc-conference.org_PASC23_sess174@linklings.com
SUMMARY:MS4G - Biophysics-Informed Machine Learning (part 2/2)
DESCRIPTION:Minisymposium\n\nFor designing personalized treatment strategi
 es, measurable quantities (biomarkers) that relate a patient’s clinical re
 presentation to the existence, progress, and outcome of the disease need t
 o be measured. They can often be formulated as quantities coming from biop
 hysical models involving, for example, material deformations or fluid tran
 sport. However, the computational cost of numerically solving for these qu
 antities can be prohibitive. These challenges are limiting the potential c
 linical impact of classical computational approaches, thus posing the need
  for new frameworks that reduce the time to prediction without sacrificing
  the physical consistency and fidelity of the inferred biomarkers. The suc
 cess of machine learning methods provides a viable path to amortize the co
 st of these expensive simulations by training models to replicate the inpu
 t-output behavior of the classical simulations. In purely data driven appr
 oaches, large amounts of labeled data are needed to train the model withou
 t leveraging any prior knowledge about the underlying biophysics. Unfortun
 ately, in many biological scenarios the data acquisition process can be ex
 pensive and time consuming, limiting the amount of available training data
 . To address this difficulty, biophysics-informed machine learning offers 
 a computationally efficient approach that has the potential to bridge the 
 gap between modeling and clinical decision making.\n\nScientific Machine L
 earning for Cardiac Electrophysiology Applications\n\nCardiac modeling for
  precision cardiology is an emerging technology in clinical practice. Than
 ks to the sophistication of state-of-the-art electrophysiology models, it 
 is possible to tailor treatments to patient characteristics, thus improvin
 g the therapeutic outcome. Patient-specific modeling requi...\n\n\nSimone 
 Pezzuto (University of Trento, Università della Svizzera italiana); Franci
 sco Sahli Costabal (Pontificia Universidad Católica de Chile); and Paris P
 erdikaris (University of Pennsylvania)\n---------------------\nAutomatic M
 odel Construction for Patient-Specific Aortic Flow Simulations Using Geome
 tric Deep Learning\n\nImage-based computational fluid dynamics (CFD) provi
 des comprehensive hemodynamic flow information and has hence been widely u
 sed in cardiovascular disease diagnosis. Reliable CFD simulation result re
 quires accurate reconstruction of the geometry from medical images such as
  computerized tomography o...\n\n\nPan Du, Delin An, Chaoli Wang, and Jian
 -Xun Wang (University of Notre Dame)\n---------------------\nA Novel Deep 
 Learning Model for Patient-Specific Computational Modeling of Cardiac Elec
 trophysiology\n\nPatient-specific computational modeling of cardiac electr
 ophysiology (EP) has been shown to have a potential utility in a wide rang
 e of arrhythmia prognostics and treatment applications. Yet, it requires e
 xtensive computational resources, posing a critical challenge for expandin
 g its application in ...\n\n\nMinglang Yin (Johns Hopkins Whiting School o
 f Engineering), Lu Lu (University of Pennsylvania), and Mauro Maggioni and
  Natalia Trayanova (Johns Hopkins Whiting School of Engineering)\n--------
 -------------\nWarpPINN: Cine-MR Image Registration with Physics-Informed 
 Neural Networks\n\nHeart failure is typically diagnosed with a global func
 tion assessment, such as ejection fraction. However, these metrics have lo
 w discriminate power, failing to distinguish different types of this disea
 se. Quantifying local deformations in the form of cardiac strain can provi
 de helpful information,...\n\n\nFrancisco Sahli Costabal (Pontificia Unive
 rsidad Católica de Chile, iHEALTH); Pablo Arratia López (University of Bat
 h); Hernán Mella (Pontificia Universidad Católica de Valparaíso); and Serg
 io Uribe and Daniel Hurtado (Pontificia Universidad Católica de Chile)\n\n
 Domain: Life Sciences\n\nSession Chair: Georgios Kissas (University of Pen
 nsylvania)
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