TY  - JOUR
AU  - Al-Maini, Mustafa
AU  - Maindarkar, Mahesh
AU  - Kitas, George D.
AU  - Khanna, Narendra N.
AU  - Misra, Durga Prasanna
AU  - Johri, Amer M.
AU  - Mantella, Laura
AU  - Agarwal, Vikas
AU  - Sharma, Aman
AU  - Singh, Inder M.
AU  - Tsoulfas, George
AU  - Laird, John R.
AU  - Faa, Gavino
AU  - Teji, Jagjit
AU  - Turk, Monika
AU  - Visković, Klaudija
AU  - Ruzsa, Zoltan
AU  - Mavrogeni, Sophie
AU  - Rathore, Vijay
AU  - Miner, Martin
AU  - Kalra, Manudeep K.
AU  - Isenović, Esma R.
AU  - Saba, Luca
AU  - Fouda, Mostafa M.
AU  - Suri, Jasjit S.
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12947
AB  - The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™–aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.
T2  - Rheumatology International
T1  - Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review
VL  - 43
IS  - 11
SP  - 1965
EP  - 1982
DO  - 10.1007/s00296-023-05415-1
ER  - 

@article{
author = "Al-Maini, Mustafa and Maindarkar, Mahesh and Kitas, George D. and Khanna, Narendra N. and Misra, Durga Prasanna and Johri, Amer M. and Mantella, Laura and Agarwal, Vikas and Sharma, Aman and Singh, Inder M. and Tsoulfas, George and Laird, John R. and Faa, Gavino and Teji, Jagjit and Turk, Monika and Visković, Klaudija and Ruzsa, Zoltan and Mavrogeni, Sophie and Rathore, Vijay and Miner, Martin and Kalra, Manudeep K. and Isenović, Esma R. and Saba, Luca and Fouda, Mostafa M. and Suri, Jasjit S.",
year = "2023",
abstract = "The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™–aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.",
journal = "Rheumatology International",
title = "Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review",
volume = "43",
number = "11",
pages = "1965-1982",
doi = "10.1007/s00296-023-05415-1"
}

Al-Maini, M., Maindarkar, M., Kitas, G. D., Khanna, N. N., Misra, D. P., Johri, A. M., Mantella, L., Agarwal, V., Sharma, A., Singh, I. M., Tsoulfas, G., Laird, J. R., Faa, G., Teji, J., Turk, M., Visković, K., Ruzsa, Z., Mavrogeni, S., Rathore, V., Miner, M., Kalra, M. K., Isenović, E. R., Saba, L., Fouda, M. M.,& Suri, J. S.. (2023). Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review. in Rheumatology International, 43(11), 1965-1982.
https://doi.org/10.1007/s00296-023-05415-1

Al-Maini M, Maindarkar M, Kitas GD, Khanna NN, Misra DP, Johri AM, Mantella L, Agarwal V, Sharma A, Singh IM, Tsoulfas G, Laird JR, Faa G, Teji J, Turk M, Visković K, Ruzsa Z, Mavrogeni S, Rathore V, Miner M, Kalra MK, Isenović ER, Saba L, Fouda MM, Suri JS. Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review. in Rheumatology International. 2023;43(11):1965-1982.
doi:10.1007/s00296-023-05415-1 .

Al-Maini, Mustafa, Maindarkar, Mahesh, Kitas, George D., Khanna, Narendra N., Misra, Durga Prasanna, Johri, Amer M., Mantella, Laura, Agarwal, Vikas, Sharma, Aman, Singh, Inder M., Tsoulfas, George, Laird, John R., Faa, Gavino, Teji, Jagjit, Turk, Monika, Visković, Klaudija, Ruzsa, Zoltan, Mavrogeni, Sophie, Rathore, Vijay, Miner, Martin, Kalra, Manudeep K., Isenović, Esma R., Saba, Luca, Fouda, Mostafa M., Suri, Jasjit S., "Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review" in Rheumatology International, 43, no. 11 (2023):1965-1982,
https://doi.org/10.1007/s00296-023-05415-1 . .

TY  - JOUR
AU  - Saba, Luca
AU  - Maindarkar, Mahesh
AU  - Khanna, Narendra N.
AU  - Johri, Amer M.
AU  - Mantella, Laura
AU  - Laird, John R.
AU  - Paraskevas, Kosmas I.
AU  - Ruzsa, Zoltan
AU  - Kalra, Manudeep K.
AU  - Fernandes, Jose Fernandes E.
AU  - Chaturvedi, Seemant
AU  - Nicolaides, Andrew
AU  - Rathore, Vijay
AU  - Singh, Narpinder
AU  - Fouda, Mostafa M.
AU  - Isenović, Esma R.
AU  - Al-Maini, Mustafa
AU  - Viswanathan, Vijay
AU  - Suri, Jasjit S.
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12023
AB  - 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 3 ) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdge TM  4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdge TM  4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.
T2  - Frontiers in Bioscience-Landmark
T1  - A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool
VL  - 28
IS  - 10
SP  - 248
DO  - 10.31083/j.fbl2810248
ER  - 

@article{
author = "Saba, Luca and Maindarkar, Mahesh and Khanna, Narendra N. and Johri, Amer M. and Mantella, Laura and Laird, John R. and Paraskevas, Kosmas I. and Ruzsa, Zoltan and Kalra, Manudeep K. and Fernandes, Jose Fernandes E. and Chaturvedi, Seemant and Nicolaides, Andrew and Rathore, Vijay and Singh, Narpinder and Fouda, Mostafa M. and Isenović, Esma R. and Al-Maini, Mustafa and Viswanathan, Vijay and Suri, Jasjit S.",
year = "2023",
abstract = "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 3 ) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdge TM  4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdge TM  4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.",
journal = "Frontiers in Bioscience-Landmark",
title = "A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool",
volume = "28",
number = "10",
pages = "248",
doi = "10.31083/j.fbl2810248"
}

Saba, L., Maindarkar, M., Khanna, N. N., Johri, A. M., Mantella, L., Laird, J. R., Paraskevas, K. I., Ruzsa, Z., Kalra, M. K., Fernandes, J. F. E., Chaturvedi, S., Nicolaides, A., Rathore, V., Singh, N., Fouda, M. M., Isenović, E. R., Al-Maini, M., Viswanathan, V.,& Suri, J. S.. (2023). A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool. in Frontiers in Bioscience-Landmark, 28(10), 248.
https://doi.org/10.31083/j.fbl2810248

Saba L, Maindarkar M, Khanna NN, Johri AM, Mantella L, Laird JR, Paraskevas KI, Ruzsa Z, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh N, Fouda MM, Isenović ER, Al-Maini M, Viswanathan V, Suri JS. A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool. in Frontiers in Bioscience-Landmark. 2023;28(10):248.
doi:10.31083/j.fbl2810248 .

Saba, Luca, Maindarkar, Mahesh, Khanna, Narendra N., Johri, Amer M., Mantella, Laura, Laird, John R., Paraskevas, Kosmas I., Ruzsa, Zoltan, Kalra, Manudeep K., Fernandes, Jose Fernandes E., Chaturvedi, Seemant, Nicolaides, Andrew, Rathore, Vijay, Singh, Narpinder, Fouda, Mostafa M., Isenović, Esma R., Al-Maini, Mustafa, Viswanathan, Vijay, Suri, Jasjit S., "A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool" in Frontiers in Bioscience-Landmark, 28, no. 10 (2023):248,
https://doi.org/10.31083/j.fbl2810248 . .