TY - JOUR
T1 - A Regression Method Based on Noninvasive Clinical Data to Predict the Mechanical Behavior of Ascending Aorta Aneurysmal Tissue
AU - Auricchio, Ferdinando
AU - Ferrara, Anna
AU - Lanzarone, Ettore
AU - Morganti, Simone
AU - Totaro, Pasquale
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Goal: Ascending aorta aneurysms represent a severe life-threatening condition associated with asymptomatic risk of rupture. Prediction of aneurysm evolution and rupture is one of the hottest investigation topics in cardiovascular science, and the decision on when and whether to surgically operate is still an open question. We propose an approach for estimating the patient-specific ultimate mechanical properties and stress-stretch characteristics based on noninvasive data. Methods: As for the characteristics, we consider a nonlinear constitutive model of the aortic wall and assume patient-specific model coefficients. Through a regression model, we build the response surfaces of ultimate stress, ultimate stretch, and model coefficients in function of patient data that are commonly available in the clinical practice. We apply the approach to a dataset of 59 patients. Results: The approach is fair and accurate response surfaces can be obtained for both ultimate properties and model coefficients. Conclusion: Prediction errors are acceptable, even though a larger patient dataset will be required to stabilize the surfaces, making it possible to apply the approach in the clinical practice. Significance: A fair prediction of the patient aortic mechanical behavior, based on clinical information noninvasively acquired, would improve the decision process and lead to more effective treatments.
AB - Goal: Ascending aorta aneurysms represent a severe life-threatening condition associated with asymptomatic risk of rupture. Prediction of aneurysm evolution and rupture is one of the hottest investigation topics in cardiovascular science, and the decision on when and whether to surgically operate is still an open question. We propose an approach for estimating the patient-specific ultimate mechanical properties and stress-stretch characteristics based on noninvasive data. Methods: As for the characteristics, we consider a nonlinear constitutive model of the aortic wall and assume patient-specific model coefficients. Through a regression model, we build the response surfaces of ultimate stress, ultimate stretch, and model coefficients in function of patient data that are commonly available in the clinical practice. We apply the approach to a dataset of 59 patients. Results: The approach is fair and accurate response surfaces can be obtained for both ultimate properties and model coefficients. Conclusion: Prediction errors are acceptable, even though a larger patient dataset will be required to stabilize the surfaces, making it possible to apply the approach in the clinical practice. Significance: A fair prediction of the patient aortic mechanical behavior, based on clinical information noninvasively acquired, would improve the decision process and lead to more effective treatments.
KW - Ascending aorta aneurysm (AsAA)
KW - response surface
KW - stress-stretch characteristics
KW - ultimate mechanical properties
KW - uniaxial tests
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U2 - 10.1109/TBME.2016.2645762
DO - 10.1109/TBME.2016.2645762
M3 - Article
AN - SCOPUS:85037058708
VL - 64
SP - 2607
EP - 2617
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
SN - 0018-9294
IS - 11
M1 - 7801847
ER -