A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Abstract

Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

“…7 . CUSIP is an image-based phenotype that uses the event equivalent gold standard (EEGS) [ 57 , 138 , 139 ] model.

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“…Recently, Jamthikar et al [ 159 , 160 ] explained the role of ML for CVD/stroke risk assessment within a big data framework by fusing image-based phenotypes and conventional risk factors for CCA and bulb segments [ 161 ]. In another study, the authors discussed the preventive cardiovascular framework for coronary artery disease management in ML [ 63 ] and the big data framework.…”

“…A disadvantage, however, is their inability to provide a “risk assessment.” The application of artificial intelligence (AI) can enhance the information provided by these imaging modalities, resulting in a more accurate characterization of the tissue and the disease process [ [45] , [46] , [47] , [48] , [49] , [50] , [51] ]. The combination of AI and medical imaging has been shown to improve diagnosis and risk stratification, speed up patient evaluation, enhance disease monitoring, and accelerate early intervention [ 40 , 48 , [52] , [53] , [54] , [55] , [56] , [57] ]. Thus, this review will focus on the use of AI-based tissue characterization of images of comorbid patients affected by COVID-19.…”

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

“…Figure 4 shows the generalized framework of supervised ML-based CVD risk assessment. In the case of CVD risk assessment, the gold standard can be (1) the primary endpoints such as presence or absence of cardiovascular events, or (2) surrogate endpoints such as cIMT, PA, and CAC score, or a combination of these risk factors [51,52,54]. Several types of input features can be used to train the AI-based algorithms.…”

“…Besides these statistically derived CVD risk calculators, artificial intelligence (AI)-based techniques are also penetrating several medical imaging and risk assessment applications [46][47][48][49][50][51][52][53][54]. AI-based algorithms such as machine learning (ML) methods provide a better CVD risk assessment when compared with statistically derived conventional risk calculators [51,55,56].…”