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DTSTAMP:20230831T095746Z
LOCATION:Sanada I
DTSTART;TZID=Europe/Stockholm:20230627T110000
DTEND;TZID=Europe/Stockholm:20230627T113000
UID:submissions.pasc-conference.org_PASC23_sess173_msa126@linklings.com
SUMMARY:Neural Operators for Detecting Aortic Aneurysm Contributors
DESCRIPTION:Minisymposium\n\nSomdatta Goswami (Brown University), David Li
  and Jay Humphrey (Yale University), and George Karniadakis (Brown Univers
 ity)\n\nThoracic aortic aneurysm is a localized dilatation of the aorta th
 at can lead to life-threatening dissection or rupture. Currently, the only
  way to assess the progression of this condition is by measuring the size 
 of the aneurysm and its growth rate. However, there is hope that by using 
 computational modeling of the aorta's biomechanics, we can predict its fut
 ure geometry and properties based on the initial causes of the condition. 
 To achieve this, we have created a framework that uses a deep operator net
 work-based surrogate model to identify factors that contribute to thoracic
  aortic aneurysms using synthetic finite-element-based datasets. We have t
 rained our model by generating maps of local aortic dilation and distensib
 ility for multiple risk factors using a constrained mixture model of aorti
 c growth and remodeling. The performance of two proposed frameworks is eva
 luated on insult distributions ranging from simple to complex. We have fou
 nd that our continuous learning approach can accurately predict a patient'
 s specific insult profile associated with any given dilation and distensib
 ility map, especially when based on greyscale full-field images. Our resul
 ts suggest that DeepONet can be used to support transfer learning of patie
 nt-specific inputs to predict the progression of thoracic aortic aneurysms
 .\n\nDomain: Life Sciences\n\nSession Chair: Georgios Kissas (University o
 f Pennsylvania)
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