An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes.

Autor: Chung TK; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA., Gueldner PH; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA., Aloziem OU; School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA., Liang NL; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.; Division of Vascular Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.; Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA., Vorp DA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; Clinical & Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA, USA. vorp@pitt.edu.; Bioengineering, Cardiothoracic Surgery, Surgery, Chemical and Petroleum Engineering and the Clinical and Translational Sciences Institute, Center for Bioengineering, University of Pittsburgh, 300 Technology Drive, Suite 300, Pittsburgh, PA, 15219, USA. vorp@pitt.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Feb 09; Vol. 14 (1), pp. 3390. Date of Electronic Publication: 2024 Feb 09.
DOI: 10.1038/s41598-024-53459-5
Abstrakt: Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their clinically-estimated risk of rupture-an event that is the 13th leading cause of death in the US-exceeds the risk associated with repair. Yet the current clinical guideline remains a one-size-fits-all "maximum diameter criterion" whereby AAA exceeding a threshold diameter is thought to make the risk of rupture high enough to warrant intervention. However, between 7 and 23.4% of smaller-sized AAA have been reported to rupture with diameters below the threshold. In this study, we train and assess machine learning models using clinical, biomechanical, and morphological indices from 381 patients to develop an aneurysm prognosis classifier to predict one of three outcomes for a given AAA patient: their AAA will remain stable, their AAA will require repair based as currently indicated from the maximum diameter criterion, or their AAA will rupture. This study represents the largest cohort of AAA patients that utilizes the first available medical image and clinical data to classify patient outcomes. The APC model therefore represents a potential clinical tool to striate specific patient outcomes using machine learning models and patient-specific image-based (biomechanical and morphological) and clinical data as input. Such a tool could greatly assist clinicians in their management decisions for patients with AAA.
(© 2024. The Author(s).)
Databáze: MEDLINE
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