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 . .