Abstract
This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs. A virtual patient database (VPD) is created using one-dimensional pulse wave propagation model of haemodynamics. Four different machine learning (ML) methods are used to train and test a series of classifiers—both binary and multiclass—to distinguish between healthy and unhealthy virtual patients (VPs) using different combinations of pressure and flow-rate measurements. It is found that the ML classifiers achieve specificities larger than 80% and sensitivities ranging from 50 to 75%. The most balanced classifier also achieves an area under the receiver operative characteristic curve of 0.75, outperforming approximately 20 methods used in clinical practice, and thus placing the method as moderately accurate. Other important observations from this study are that (i) few measurements can provide similar classification accuracies compared to the case when more/all the measurements are used; (ii) some measurements are more informative than others for classification; and (iii) a modification of standard methods can result in detection of not only the presence of stenosis, but also the stenosed vessel.Graphical An overview of methodology fo the creation of virtual patients and their classification
Highlights
While there are many forms of arterial disease, one of the most common is stenosis, which refers to the narrowing of an arterial vessel
The wide application and success of machine learning (ML) methods in medical applications motivates exploration of their use for stenosis detection. The aim of this proof of concept (PoC) study is to carry out an initial investigation into the potential of using ML classification algorithms to predict the presence of stenosis, using haemodynamics measurements
Given a situation in which there is an equal number of healthy and unhealthy virtual patients (VPs), an entire network binary classifiers (ENBCs) which correctly predicts the health of 80% of healthy VPs (R = Se = 0.8) and 20% of unhealthy VPs (Sp = 0.2) will achieve an F1 score of 0.61
Summary
While there are many forms of arterial disease, one of the most common is stenosis, which refers to the narrowing of an arterial vessel. A previous study [44] has explored the use of physicsbased models of pulsewave propagation to predict the presence of an aneurysm, another common form of arterial disease, using flow-rate measurements. The wide application and success of ML methods in medical applications motivates exploration of their use for stenosis detection The aim of this proof of concept (PoC) study is to carry out an initial investigation into the potential of using ML classification algorithms to predict the presence of stenosis, using haemodynamics measurements. The results and analysis of the ML methods performance are presented, with a focus on uncovering why some ML methods perform better than others and which measurements (and their combinations) are more informative
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