Key result
A trained machine learning model successfully classified normal and abdominal aortic aneurysm conditions using digital photoplethysmogram pulse waves from an in silico database.
Why the study?
Early detection of abdominal aortic aneurysms is critical to reduce mortality but most are found incidentally, motivating non-invasive screening methods.
Does machine learning-based pulse wave analysis improve the early detection of abdominal aortic aneurysms in simulated subjects?
Does machine learning-based pulse wave analysis improve the early detection of abdominal aortic aneurysms in simulated subjects?
Machine learning-based pulse wave analysis of in silico data demonstrates feasibility for early detection of abdominal aortic aneurysms, suggesting potential for future wearable device screening.
In silico success does not change practice; leaves open human validation of ML pulse wave analysis for AAA screening.
An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs – identified by parameter sensitivity analysis – in an existing database of in silico PWs representative of subjects without AAA. Then, a machine learning architecture for early detection of AAAs was proposed, which was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties that significantly influence the PW. The simulated PW indexes extracted from the database showed that the PW was not only influenced by the presence of an AAA but was also significantly affected by multiple cardiovascular parameters that compromised the detection of AAAs by using individual PW indexes. Alternatively, the trained machine learning model performed well in classifying normal and AAA conditions using digital photoplethysmogram PWs from the database. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.
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Wang et al. (2021) studied Abdominal aortic aneurysm. Machine learning-based pulse wave analysis vs. Normal conditions (no AAA) was evaluated on Classification of normal and AAA conditions. A trained machine learning model successfully classified normal and abdominal aortic aneurysm conditions using digital photoplethysmogram pulse waves from an in silico database.
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