Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review

JS Suri, M Bhagawati, S Paul, A Protogeron… - Computers in biology …, 2022 - Elsevier
Abstract Background Artificial Intelligence (AI), in particular, machine learning (ML) has
shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) …

Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited?

L Saba, A Jamthikar, D Gupta, NN Khanna… - International …, 2019 - iris.unica.it
Carotid intima-media thickness (cIMT) and carotid plaque (CP) currently act as risk
predictors for CVD/Stroke risk assessment. Over 2000 articles have been published that …

Unseen artificial intelligence—Deep learning paradigm for segmentation of low atherosclerotic plaque in carotid ultrasound: A multicenter cardiovascular study

PK Jain, N Sharma, L Saba, KI Paraskevas, MK Kalra… - Diagnostics, 2021 - mdpi.com
Background: The early detection of carotid wall plaque is recommended in the prevention of
cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode …

Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for …

M Biswas, L Saba, S Chakrabartty, NN Khanna… - Computers in biology …, 2020 - Elsevier
Motivation The early screening of cardiovascular diseases (CVD) can lead to effective
treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque …

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

A Jamthikar, D Gupta, L Saba… - Cardiovascular …, 2020 - pmc.ncbi.nlm.nih.gov
Background Statistically derived cardiovascular risk calculators (CVRC) that use
conventional risk factors, generally underestimate or overestimate the risk of cardiovascular …

A multicenter study on carotid ultrasound plaque tissue characterization and classification using six deep artificial intelligence models: a stroke application

L Saba, SS Sanagala, SK Gupta… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Atherosclerotic plaque in carotid arteries can ultimately lead to cerebrovascular events if not
monitored. The objectives of this study are (a) to design a set of artificial intelligence (AI) …

[HTML][HTML] Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with …

A Jamthikar, D Gupta, NN Khanna, L Saba, JR Laird… - Indian heart …, 2020 - Elsevier
Motivation Machine learning (ML)-based stroke risk stratification systems have typically
focused on conventional risk factors (CRF)(AtheroRisk-conventional). Besides CRF, carotid …

Ensemble machine learning and its validation for prediction of coronary artery disease and acute coronary syndrome using focused carotid ultrasound

AD Jamthikar, D Gupta, LE Mantella… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
The objective of this study is to demonstrate the effectiveness of ensemble-learning-driven
machine learning (EML) algorithms over the conventional ML (CML) algorithms in predicting …

A review on atherosclerotic biology, wall stiffness, physics of elasticity, and its ultrasound-based measurement

AK Patel, HS Suri, J Singh, D Kumar… - Current atherosclerosis …, 2016 - Springer
Functional and structural changes in the common carotid artery are biomarkers for
cardiovascular risk. Current methods for measuring functional changes include pulse wave …

Preclinical carotid atherosclerosis as an indicator of polyvascular disease: a narrative review

P Poredos, MK Jezovnik - Annals of Translational Medicine, 2021 - pmc.ncbi.nlm.nih.gov
Carotid atherosclerotic lesions are correlated with atherosclerotic deterioration of the arterial
wall in other vascular territories and with cardiovascular events. The detection of pre …