A machine learning approach for predicting complications in descending and thoracoabdominal aortic aneurysms

Ostberg, Nicolai P; Zafar, Mohammad A.; Mukherjee, Sandip; Ziganshin, Bulat A.; Elefteriades, John A.

doi:10.1016/j.jtcvs.2021.12.045

Cited by 14 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) It has been shown that aortic geometry alone is not sufficient to predict TAA progression [22]. Many have thus sought to incorporate additional information such as biomechanical properties and patient-level variables to improve predictive capability.…”

Section: Discussionmentioning

confidence: 99%

“…The results shown in table 4 indicate that the proposed frameworks indeed provide effective predictions for both analytically defined and randomly generated insult profiles. Our observations are summarized as follows: It has been shown that aortic geometry alone is not sufficient to predict TAA progression [22]. Many have thus sought to incorporate additional information such as biomechanical properties and patient-level variables to improve predictive capability.…”

Section: Discussionmentioning

confidence: 99%

“…Current advancements in modelling now provide the opportunity to leverage machine learning, which has emerged as an effective surrogate model for high-fidelity solvers, in order to overcome previous computational hurdles that would otherwise make such modelling intractable for clinically relevant time frames. Such surrogate models have demonstrated the potential for automated measurement of aortic geometry, patient risk stratification, and prediction of aneurysm growth and rupture [6,[21][22][23][24][25]. Additionally, physics-informed neural networks (PINNs) [26][27][28][29] have been a promising advancement in the domain of scientific machine learning.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Goswami

Rego

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta that can lead to life-threatening dissection or rupture. In vivo assessments of TAA progression are largely limited to measurements of aneurysm size and growth rate. There is promise, however, that computational modelling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating mechanobiological insults. We present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify TAA contributing factors using synthetic finite-element-based datasets. For training, we employ a constrained mixture model of aortic growth and remodelling to generate maps of local aortic dilatation and distensibility for multiple TAA risk factors. We evaluate the performance of the surrogate model for insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We propose two frameworks, one trained on sparse information and one on full-field greyscale images, to gain insight into a preferred neural operator-based approach. We show that this continuous learning approach can predict the patient-specific insult profile associated with any given dilatation and distensibility map with high accuracy, particularly when based on full-field images. Our findings demonstrate the feasibility of applying DeepONet to support transfer learning of patient-specific inputs to predict TAA progression.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Goswami

Rego

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…1. It has been shown that aortic geometry alone is not sufficient to predict TAA progression [21]. Many have thus sought to incorporate additional information such as biomechanical properties and patient-level variables to improve predictive capability.…”

Section: Insult Profile Prediction With Sensor Point-and Image-based ...mentioning

confidence: 99%

“…Current advancements in modeling now provide the opportunity to leverage machine learning, which has emerged as an effective surrogate model for high-fidelity solvers, in order to overcome previous computational hurdles that would otherwise make such modeling intractable for clinically relevant time frames. Such surrogate models have demonstrated the potential for automated measurement of aortic geometry, patient risk stratification, and the prediction of aneurysm growth and rupture [6,[20][21][22][23][24]. Additionally, physics-informed neural networks (PINNs) [25][26][27][28] have been a promising advancement in the domain of scientific machine learning.…”

Section: Introductionmentioning

confidence: 99%

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Goswami¹,

Li²,

Rego³

et al. 2022

Preprint

View full text Add to dashboard Cite

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture. Several genetic mutations and predisposing factors that contribute to TAA have been studied in mouse models to characterize specific changes in aortic microstructure and material properties that result from a wide range of mechanobiological insults. By contrast, assessments of TAA progression in vivo are largely limited to measurements of aneurysm size and growth rate. It has been shown, however, that aortic geometry alone is not sufficient to predict the patient-specific progression of TAA but computational modeling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating insults. In this work, we present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify contributing factors for TAA by using synthetic finite element-based datasets of aortic growth and remodeling (G&R) resulting from prescribed mechanobiological insults. For training data, we investigate multiple types of TAA risk factors and spatial distributions within a computationally efficient constrained mixture model to generate axial-azimuthal maps of aortic dilatation and distensibility. The trained network is then capable of predicting the initial distribution and extent of the insult from a given set of dilatation and distensibility information, which in turn can be used to determine subsequent aortic geometry and mechanical properties. Two DeepONet frameworks are proposed, one trained on sparse information and one on full-field grayscale images, to gain insight into a preferred neural operator-based approach. Performance of the surrogate models is evaluated through multiple simulations carried out on insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We show that this integrated continuous learning modeling approach can predict the patient-specific mechanobiological insult profile associated with any given dilatation and distensibility map with a high accuracy, particularly when based on full-field images. Our findings demonstrate the feasibility of

show abstract

Künstliche Intelligenz in der Gefäßchirurgie

Behrendt,

Gombert,

Uhl

et al. 2024

Gefässchirurgie

View full text Add to dashboard Cite

A machine learning approach for predicting complications in descending and thoracoabdominal aortic aneurysms

Cited by 14 publications

References 27 publications

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Künstliche Intelligenz in der Gefäßchirurgie

Contact Info

Product

Resources

About