128-Detector-Row Computed Tomography coronary angiography assessing differences in morphology and distribution of atherosclerotic plaques between patients with and without pre-test probability of significant coronary artery disease

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

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.

Plaque stability and the southern European paradox

Differences between European countries in coronary heart disease mortality were initially described in the 20th century, and albeit less dramatic than first reported, these differences remain substantial. Three main hypotheses have been proposed to explain the so-called "Mediterranean paradox": a) underestimation of coronary heart disease mortality due to methodological flaws; b) the "lag time" hypothesis, and c) the traditional Mediterranean diet and lifestyle. In this manuscript we present and discuss another possible explanation for the Mediterranean paradox related to the higher prevalence and and incidence of stable atheromatous plaques in this area.