Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK.
School of Engineering, Deakin University, Geelong, 3216, Australia.
Int J Comput Assist Radiol Surg. 2022 Dec;17(12):2221-2229. doi: 10.1007/s11548-022-02725-w. Epub 2022 Aug 10.
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.
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.
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.
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.
动脉粥样硬化在冠状动脉瘤（CAA）的发生中起着重要作用。尽管这种血管疾病的治疗选择正在发展，但在选择和应用足够的医疗解决方案方面仍然存在挑战和局限性。对于新型疗法的手术干预，基于非侵入性特定患者的研究可能会带来更有希望的结果。尽管进行了医学和病理学检查，但这些术前研究需要特殊的生物医学和计算机辅助工程技术。在这项研究中，首次提出了一种用于非侵入性检测动脉粥样硬化性 CAA 的机器学习（ML）模型。
研究数据库是从 61 名患者的 80 个 CAA 的血流动力学分析和计算机断层血管造影（CTA）中收集的，该研究已获得机构审查委员会（IRB）的批准。所提出的 ML 模型是通过单类支持向量机（1SVM）进行学习的，1SVM 是机器学习的一个领域，为异常值和异常检测提供技术。
应用的 ML 算法在设计用于非侵入性诊断动脉粥样硬化性动脉瘤的程序方面产生了合理的结果，具有较高且显著的准确性。该方法可以作为一种独特的人工智能（AI）工具，用于未来在手术干预治疗方法的临床决策过程中提供保证。
由于一些临床决策，非侵入性诊断动脉粥样硬化性 CAA 是血管内手术成功的重要因素之一。尽管没有用于管理这种诊断的精确工具，但本研究提出了一种 ML 模型，该模型可以降低血管内手术失败、死亡风险和术后并发症的概率。该模型能够提高临床决策的准确性，以选择低风险的治疗方案。
