Artificial intelligence techniques may innovate patient selection for thoracic endovascular aortic repair

Maurizio Domanin^{1

2}, Tim J Mandigers^{2

3}, Alfio M Quarteroni⁴, Santi Trimarchi^{1

2}

Affiliations

¹ Clinical and Community Sciences Department, Università degli Studi di Milano, Milan, Italy.
² Department of Vascular Surgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.
³ Department of Vascular Surgery, University Medical Center Utrecht, Utrecht, Netherlands.
⁴ Modelling and Scientific Computing (MOX), Department of Mathematics, Politecnico di Milano, Milan, Italy.

Editorial

Maurizio Domanin et al. Eur J Cardiothorac Surg. 2022.

. 2022 Nov 3;62(6):ezac526.

doi: 10.1093/ejcts/ezac526.

Maurizio Domanin^{1

2}, Tim J Mandigers^{2

3}, Alfio M Quarteroni⁴, Santi Trimarchi^{1

2}

¹ Clinical and Community Sciences Department, Università degli Studi di Milano, Milan, Italy.
² Department of Vascular Surgery, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.
³ Department of Vascular Surgery, University Medical Center Utrecht, Utrecht, Netherlands.
⁴ Modelling and Scientific Computing (MOX), Department of Mathematics, Politecnico di Milano, Milan, Italy.

No abstract available

Keywords: Artificial intelligence; Lumbar sarcopenia; Machine learning; Thoracic endovascular aortic repair; Type B aortic dissection.

Comment on

The impact of machine-learning-derived lean psoas muscle area on prognosis of type B aortic dissection patients undergoing endovascular treatment.
Liu J, Su S, Liu W, Xie E, Hu X, Lin W, Ding H, Luo S, Liu Y, Huang W, Li J, Yang F, Luo J. Liu J, et al. Eur J Cardiothorac Surg. 2022 Nov 3;62(6):ezac482. doi: 10.1093/ejcts/ezac482. Eur J Cardiothorac Surg. 2022. PMID: 36205612