A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm

Rostam-Alilou, AA, Safari, M, Jarrah, HR, Zolfagharian, A and Bodaghi, M ORCID: https://orcid.org/0000-0002-0707-944X, 2022. A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm. International Journal of Computer Assisted Radiology and Surgery. ISSN 1861-6410

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Abstract

Purpose: Atherosclerosis plays a significant role in the initiation of coronary artery aneurysms (CAA). Although the treatment options for this kind of vascular disease are developing, there are challenges and limitations in both selecting and applying sufficient medical solutions. For surgical interventions, that are novel therapies, non-invasive specific patient-based studies could lead to obtaining more promising results. Despite medical and pathological tests, these pre-surgical investigations require special biomedical and computer-aided engineering techniques. In this study, a machine learning (ML) model is proposed for the non-invasive detection of atherosclerotic CAA for the first time.

Methods: The database for study was collected from hemodynamic analysis and computed tomography angiography (CTA) of 80 CAAs from 61 patients, approved by the Institutional Review Board (IRB). The proposed ML model is formulated for learning by a one-class support vector machine (1SVM) that is a field of ML to provide techniques for outlier and anomaly detection.

Results: The applied ML algorithms yield reasonable results with high and significant accuracy in designing a procedure for the non-invasive diagnosis of atherosclerotic aneurysms. This proposed method could be employed as a unique artificial intelligence (AI) tool for assurance in clinical decision-making procedures for surgical intervention treatment methods in the future.

Conclusions: The non-invasive diagnosis of the atherosclerotic CAAs, which is one of the vital factors in the accomplishment of endovascular surgeries, is important due to some clinical decisions. Although there is no accurate tool for managing this kind of diagnosis, an ML model that can decrease the probability of endovascular surgical failures, death risk, and post-operational complications is proposed in this study. The model is able to increase the clinical decision accuracy for low-risk selection of treatment options.

Item Type:	Journal article
Publication Title:	International Journal of Computer Assisted Radiology and Surgery
Creators:	Rostam-Alilou, A.A., Safari, M., Jarrah, H.R., Zolfagharian, A. and Bodaghi, M.
Publisher:	Springer
Date:	10 August 2022
ISSN:	1861-6410
Identifiers:	Number Type 10.1007/s11548-022-02725-w DOI 1590497 Other
Rights:	© the author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	11 Aug 2022 14:05
Last Modified:	11 Aug 2022 14:05
URI:	https://irep.ntu.ac.uk/id/eprint/46860

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