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1. Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort

Author

Bhagawati, Mrinalini, Paul, Sudip, Mantella, Laura, Johri, Amer M., Laird, John R., Singh, Inder M., Singh, Rajesh, Garg, Deepak, Fouda, Mostafa M., Khanna, Narendra N., Cau, Riccardo, Abraham, Ajith, Al-Maini, Mostafa, Isenovic, Esma R., Sharma, Aditya M., Fernandes, Jose Fernandes E., Chaturvedi, Seemant, Karla, Mannudeep K., Nicolaides, Andrew, Saba, Luca, and Suri, Jasjit S.

Published
2024
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2. Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Author

Singh, Manasvi, Kumar, Ashish, Khanna, Narendra N., Laird, John R., Nicolaides, Andrew, Faa, Gavino, Johri, Amer M., Mantella, Laura E., Fernandes, Jose Fernandes E., Teji, Jagjit S., Singh, Narpinder, Fouda, Mostafa M., Singh, Rajesh, Sharma, Aditya, Kitas, George, Rathore, Vijay, Singh, Inder M., Tadepalli, Kalyan, Al-Maini, Mustafa, Isenovic, Esma R., Chaturvedi, Seemant, Garg, Deepak, Paraskevas, Kosmas I., Mikhailidis, Dimitri P., Viswanathan, Vijay, Kalra, Manudeep K., Ruzsa, Zoltan, Saba, Luca, Laine, Andrew F., Bhatt, Deepak L., and Suri, Jasjit S.

Published
2024
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3. UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review

Author

Saba, Luca, primary, Maindarkar, Mahesh, primary, Johri, Amer M., primary, Mantella, Laura, primary, Laird, John R., primary, Khanna, Narendra N., primary, Paraskevas, Kosmas I., primary, Ruzsa, Zoltan, primary, Kalra, Manudeep K., primary, Fernandes, Jose Fernandes E, primary, Chaturvedi, Seemant, primary, Nicolaides, Andrew, primary, Rathore, Vijay, primary, Singh, Narpinder, primary, Isenovic, Esma R., primary, Viswanathan, Vijay, primary, Fouda, Mostafa M., primary, and Suri, Jasjit S., primary

Published
2024
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5. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Author

Khanna, Narendra N, Singh, Manasvi, Maindarkar, Mahesh, Kumar, Ashish, Johri, Amer M., Mentella, Laura, Laird, John R, Paraskevas, Kosmas I., Ruzsa, Zoltan, Singh, Narpinder, Kalra, Mannudeep K., Fernandes, Jose Fernandes E., Chaturvedi, Seemant, Nicolaides, Andrew, Rathore, Vijay, Singh, Inder, Teji, Jagjit S., Al-Maini, Mostafa, Isenović, Esma R., Viswanathan, Vijay, Khanna, Puneet, Fouda, Mostafa M., Saba, Luca, Suri, Jasjit S., Khanna, Narendra N, Singh, Manasvi, Maindarkar, Mahesh, Kumar, Ashish, Johri, Amer M., Mentella, Laura, Laird, John R, Paraskevas, Kosmas I., Ruzsa, Zoltan, Singh, Narpinder, Kalra, Mannudeep K., Fernandes, Jose Fernandes E., Chaturvedi, Seemant, Nicolaides, Andrew, Rathore, Vijay, Singh, Inder, Teji, Jagjit S., Al-Maini, Mostafa, Isenović, Esma R., Viswanathan, Vijay, Khanna, Puneet, Fouda, Mostafa M., Saba, Luca, and Suri, Jasjit S.

Abstract

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Published
2023

6. A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Author

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., 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, and Suri, Jasjit S.

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.

Published
2023

7. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Author

Khanna, Narendra N, primary, Singh, Manasvi, additional, Maindarkar, Mahesh, additional, Kumar, Ashish, additional, Johri, Amer M., additional, Mentella, Laura, additional, Laird, John R, additional, Paraskevas, Kosmas I., additional, Ruzsa, Zoltan, additional, Singh, Narpinder, additional, Kalra, Mannudeep K., additional, Fernandes, Jose Fernandes E., additional, Chaturvedi, Seemant, additional, Nicolaides, Andrew, additional, Rathore, Vijay, additional, Singh, Inder, additional, Teji, Jagjit S., additional, Al-Maini, Mostafa, additional, Isenovic, Esma R., additional, Viswanathan, Vijay, additional, Khanna, Puneet, additional, Fouda, Mostafa M., additional, Saba, Luca, additional, and Suri, Jasjit S., additional

Published
2023
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8. Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Author

Khanna, Narendra N., primary, Maindarkar, Mahesh A., additional, Viswanathan, Vijay, additional, Fernandes, Jose Fernandes E, additional, Paul, Sudip, additional, Bhagawati, Mrinalini, additional, Ahluwalia, Puneet, additional, Ruzsa, Zoltan, additional, Sharma, Aditya, additional, Kolluri, Raghu, additional, Singh, Inder M., additional, Laird, John R., additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Saba, Luca, additional, Agarwal, Vikas, additional, Sharma, Aman, additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Rathore, Vijay, additional, Naidu, Subbaram, additional, Liblik, Kiera, additional, Johri, Amer M., additional, Turk, Monika, additional, Mohanty, Lopamudra, additional, Sobel, David W., additional, Miner, Martin, additional, Viskovic, Klaudija, additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Kitas, George D., additional, Fouda, Mostafa M., additional, Chaturvedi, Seemant, additional, Kalra, Mannudeep K., additional, and Suri, Jasjit S., additional

Published
2022
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10. The burden of carotid-related strokes

Author

Paraskevas, Kosmas I., primary, Mikhailidis, Dimitri P., additional, Baradaran, Hediyeh, additional, Bokkers, Reinoud P. H., additional, Davies, Alun H., additional, Eckstein, Hans-Henning, additional, Faggioli, Gianluca, additional, Fernandes, Jose Fernandes E, additional, Gargiulo, Mauro, additional, Jawien, Arkadiusz, additional, Jezovnik, Mateja K., additional, Kakkos, Stavros K., additional, Knoflach, Michael, additional, Kooi, M. Eline, additional, Lanza, Gaetano, additional, Liapis, Christos D., additional, Loftus, Ian M., additional, Mansilha, Armando, additional, Mechtouff, Laura, additional, Millon, Antoine, additional, Myrcha, Piotr, additional, Nicolaides, Andrew N., additional, Pini, Rodolfo, additional, Poredos, Pavel, additional, Ricco, Jean-Baptiste, additional, Rundek, Tatjana, additional, Saba, Luca, additional, Silvestrini, Mauro, additional, Spinelli, Francesco, additional, Stilo, Francesco, additional, Sultan, Sherif, additional, Suri, Jasjit S., additional, Svetlikov, Alexei V., additional, Wijeratne, Tissa, additional, Zeebregts, Clark J., additional, and Gloviczki, Peter, additional

Published
2022
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11. Optimal Management of Asymptomatic Carotid Stenosis: Counterbalancing the Benefits with the Potential Risks

Author

Paraskevas, Kosmas I., primary, Mikhailidis, Dimitri P., additional, Baradaran, Hediyeh, additional, Davies, Alun H., additional, Eckstein, Hans-Henning, additional, Faggioli, Gianluca, additional, Fernandes, Jose Fernandes e, additional, Gupta, Ajay, additional, Jezovnik, Mateja K., additional, Kakkos, Stavros K., additional, Katsiki, Niki, additional, Kooi, M. Eline, additional, Lanza, Gaetano, additional, Liapis, Christos D., additional, Loftus, Ian M., additional, Millon, Antoine, additional, Nicolaides, Andrew N., additional, Poredos, Pavel, additional, Pini, Rodolfo, additional, Ricco, Jean-Baptiste, additional, Rundek, Tatjana, additional, Saba, Luca, additional, Spinelli, Francesco, additional, Stilo, Francesco, additional, Sultan, Sherif, additional, Zeebregts, Clark J., additional, and Chaturvedi, Seemant, additional

Published
2022
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12. Optimal periprocedural antithrombotic treatment in carotid interventions: An international, multispecialty, expert review and position statement.

Author

Paraskevas, Kosmas I., Gloviczki, Peter, Mikhailidis, Dimitri P., Antignani, Pier Luigi, Dardik, Alan, Eckstein, Hans-Henning, Faggioli, Gianluca, Fernandes, Jose Fernandes E., Fraedrich, Gustav, Gupta, Ajay, Jawien, Arkadiusz, Jezovnik, Mateja K., Kakkos, Stavros K., Knoflach, Michael, Lal, Brajesh K., Lanza, Gaetano, Liapis, Christos D., Loftus, Ian M., Mansilha, Armando, and Millon, Antoine

Abstract

Background: The optimal antithrombotic (antiplatelet or anticoagulant) treatment of patients undergoing extracranial carotid artery interventions is a subject of debate. The aim of this multidisciplinary document was to critically review the recommendations of current guidelines, taking into consideration the results of recendy published studies. Methods:The various antithrombotic strategies reported were evaluated for asymptomatic and symptomatic patients undergoing extracranial carotid artery interventions (endarterectomy, transfemoral carotid artery stenting [CAS] or transcarotid artery revascularization [TCAR]). Based on a critical review, a series of recommendations were formulated by an international expert panel. Results: For asymptomatic patients, we recommend low-dose aspirin (75-100 ing/day) or dopidogrel (75 mg/day) with the primary goal to reduce the risk of myocardial infarction and cardiovascular event rates rather than to reduce the risk of stroke. For symptomatic patients, we recommend dual antiplatelet treatment (DAFT) initiated within 24 h of the index event to reduce the risk of recurrent events. We suggest that following transfemoral CAS or TCAR, patients continue DAFT for 1 month after which a single antiplatelet agent is used. High level of evidence to support anticoagulant treatment for patients with carotid artery disease is lacking. Condusions: The antithrombotic treatment offered to carotid patients should be individualized, taking into account the presence of symptoms, the type of intervention and the goal of the treatment. The duration and type of DAFr (ticagrelor instead of dopidogrel) should be evaluated in future trials. [ABSTRACT FROM AUTHOR]

Published
2022
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13. Management of Patients with Asymptomatic Carotid Stenosis May Need to Be Individualized: A Multidisciplinary Call for Action

Author

Paraskevas, Kosmas I., primary, Mikhailidis, Dimitri P., additional, Baradaran, Hediyeh, additional, Davies, Alun H., additional, Eckstein, Hans-Henning, additional, Faggioli, Gianluca, additional, Fernandes, Jose Fernandes e, additional, Gupta, Ajay, additional, Jezovnik, Mateja K., additional, Kakkos, Stavros K., additional, Katsiki, Niki, additional, Kooi, M. Eline, additional, Lanza, Gaetano, additional, Liapis, Christos D., additional, Loftus, Ian M., additional, Millon, Antoine, additional, Nicolaides, Andrew N., additional, Poredos, Pavel, additional, Pini, Rodolfo, additional, Ricco, Jean-Baptiste, additional, Rundek, Tatjana, additional, Saba, Luca, additional, Spinelli, Francesco, additional, Stilo, Francesco, additional, Sultan, Sherif, additional, Zeebregts, Clark J., additional, and Chaturvedi, Seemant, additional

Published
2021
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16. A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool.

Author

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, Isenovic ER, Al-Maini M, Viswanathan V, and Suri JS

Subjects
Humans, Artificial Intelligence, Risk Assessment, Biomarkers, Pharmaceutical Preparations, Atherosclerosis diagnosis, Stroke genetics, Stroke prevention & control, Myocardial Infarction complications
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 (aiP3) 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 AtheroEdgeTM 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 AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm., Competing Interests: Dr. Suri and Dr. Maindarkar is with AtheroPoint™ LLC, Roseville, CA, USA, which does cardiovascular and stroke imaging. The authors declare no conflict of interest., (© 2023 The Author(s). Published by IMR Press.)

Published
2023
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