Background: Cardiovascular disease (CVD) is challenging to diagnose and
treat since symptoms appear late during the progression of atherosclerosis.
Conventional risk factors alone are not always sufficient to properly
categorize at-risk patients, and clinical risk scores are inadequate in
predicting cardiac events. Integrating genomic-based biomarkers (GBBM)
found in plasma/serum samples with novel non-invasive radiomics-based
biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height
can improve composite CVD risk prediction in the pharmaceutical paradigm. These
biomarkers consider several pathways involved in the pathophysiology of
atherosclerosis disease leading to CVD. Objective: This review proposes
two hypotheses: (i) The composite biomarkers are strongly correlated and can be
used to detect the severity of CVD/Stroke precisely, and (ii) an
explainable artificial intelligence (XAI)-based composite risk
CVD/Stroke model with survival analysis using deep learning (DL) can predict in
preventive, precision, and personalized (aiP
Announcements
Open Access
Review
A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool
Luca Saba1, Mahesh Maindarkar2,3, Narendra N. Khanna4, Amer M. Johri5, Laura Mantella6, John R. Laird7, Kosmas I. Paraskevas8, Zoltan Ruzsa9, Manudeep K. Kalra10, Jose Fernandes E. Fernandes11, Seemant Chaturvedi12, Andrew Nicolaides13, Vijay Rathore14, Narpinder Singh15, Mostafa M. Fouda16, Esma R. Isenovic17, Mustafa Al-Maini18, Vijay Viswanathan19, Jasjit S. Suri2,20,*
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1
Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
2
Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
3
School of Bioengineering Research and Sciences, Maharashtra Institute of Technology's Art, Design and Technology University, 412021 Pune, India
4
Department of Cardiology, Indraprastha APOLLO Hospitals, 110001 New Delhi, India
5
Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON K7L 1C2, Canada
6
Department of Medicine, Division of Cardiology, University of Toronto, Toronto, ON M5S 1A1, Canada
7
Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
8
Department of Vascular Surgery, Central Clinic of Athens, 11526 Athens, Greece
9
Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary
10
Department of Radiology, Harvard Medical School, Boston, MA 02118, USA
11
Department of Vascular Surgery, University of Lisbon, 1600-209 Lisbon, Portugal
12
Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD 21201, USA
13
Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, 2368 Agios Dometios, Cyprus
14
Nephrology Department, Kaiser Permanente, Sacramento, CA 95823, USA
15
Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, 248002 Uttarakhand, India
16
Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
17
Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 11001 Belgrade, Serbia
18
Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
19
MV Diabetes Centre, Royapuram, Chennai, 600013 Tamil Nadu, India
20
Department of Computer Engineering, Graphic Era Deemed to be University, 248002 Dehradun, India
*Correspondence: jasjit.suri@atheropoint.com (Jasjit S. Suri)
Front. Biosci. (Landmark Ed) 2023, 28(10), 248;
https://doi.org/10.31083/j.fbl2810248
Submitted: 10 May 2023 | Revised: 2 August 2023 | Accepted: 28 August 2023 | Published: 19 October 2023
(This article belongs to the Special Issue Explainable Artificial Intelligence in Biomedicine)
Copyright: © 2023 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract
Keywords
cardiovascular disease
stroke
biomarkers
radiomics
genomics
deep learning
bias
pruning
cloud
multicenter
pharmaceutical
explainable artificial intelligence
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