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Chung TK, Gueldner PH, Aloziem OU, Liang NL, Vorp DA. An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes. Sci Rep 2024; 14:3390. [PMID: 38336915 PMCID: PMC10858046 DOI: 10.1038/s41598-024-53459-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/31/2024] [Indexed: 02/12/2024] Open
Abstract
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.
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Affiliation(s)
- Timothy K Chung
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Pete H Gueldner
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Okechukwu U Aloziem
- School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Nathan L Liang
- 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
| | - David A Vorp
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical & Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for Vascular Remodeling and Regeneration, University of Pittsburgh, Pittsburgh, PA, USA.
- 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.
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Chung TK, Gueldner PH, Kickliter TM, Liang NL, Vorp DA. An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms. Bioengineering (Basel) 2022; 9:601. [PMID: 36354512 PMCID: PMC9687639 DOI: 10.3390/bioengineering9110601] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 10/09/2022] [Accepted: 10/20/2022] [Indexed: 07/02/2024] Open
Abstract
(1) Abdominal aortic aneurysm (AAA) biomechanics-based metrics often reported may be over/under-estimated by including non-aneurysmal regions in the analyses, which is typical, rather than isolating the dilated sac region. We demonstrate the utility of a novel sac-isolation algorithm by comparing peak/mean wall stress (PWS, MWS), with/without sac isolation, for AAA that were categorized as stable or unstable in 245 patient CT image sets. (2) 245 patient computed tomography images were collected, segmented, meshed, and had subsequent finite element analysis performed in preparation of our novel sac isolation technique. Sac isolation was initiated by rotating 3D surfaces incrementally, extracting 2D projections, curve fitting a Fourier series, and taking the local extrema as superior/inferior boundaries for the aneurysmal sac. The PWS/MWS were compared pairwise using the entire aneurysm and the isolated sac alone. (3) MWS, not PWS, was significantly different between the sac alone and the entire aneurysm. We found no statistically significant difference in wall stress measures between stable (n = 222) and unstable (n = 23) groups using the entire aneurysm. However, using sac-isolation, PWS (24.6 ± 7.06 vs. 20.5 ± 8.04 N/cm2; p = 0.003) and MWS (12.0 ± 3.63 vs. 10.5 ± 4.11 N/cm2; p = 0.022) were both significantly higher in unstable vs. stable groups. (4) Our results suggest that evaluating only the AAA sac can influence wall stress metrics and may reveal differences in stable and unstable groups of aneurysms that may not otherwise be detected when the entire aneurysm is used.
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Affiliation(s)
- Timothy K. Chung
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Pete H. Gueldner
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Trevor M. Kickliter
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Nathan L. Liang
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Division of Vascular Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - David A. Vorp
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Clinical and Translational Sciences Institute, University of Pittsburgh, Pittsburgh, PA 15213, USA
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