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3. 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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6. Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

Author

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, Viskovic, Klaudija, Ruzsa, Zoltan, Mavrogeni, Sophie, Rathore, Vijay, Miner, Martin, Kalra, Manudeep K., Isenovic, Esma R., Saba, Luca, Fouda, Mostafa M., and Suri, Jasjit S.

Published
2023
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7. 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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9. Characterization of COVID-19 severity in infected lungs via artificial intelligence transfer learning

Author

Skandha, Sanagala S, primary, Saba, Luca, additional, Gupta, Suneet K, additional, Kumar, Vijaya K, additional, Johri, Amer M, additional, Khanna, Narendra N, additional, Mavrogeni, Sophie, additional, Laird, John R, additional, Pareek, Gyan, additional, Sfikakis, Petros P, additional, Protogerou, Athanasios, additional, Turk, Monica, additional, Sharma, Aditya M, additional, Nicolaides, Andrew, additional, Kitas, George D, additional, Viskovic, Klaudija, additional, Omerzu, Tomaz, additional, and Suri, Jasjit S, additional

Published
2022
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10. Artificial intelligence based carotid plaque tissue characterisation and classification from ultrasound images using a deep learning paradigm

Author

Saba, Luca, primary, Skandha, Sanagala S, additional, Gupta, Suneet K, additional, Puvvula, Anudeep, additional, Koppula, Vijaya K, additional, Johri, Amer M, additional, Khanna, Narendra N, additional, Mavrogeni, Sophie, additional, Laird, John R, additional, Pareek, Gyan, additional, Miner, Martin, additional, Sfikakis, Petros P, additional, Protogerou, Athanasios, additional, Misra, Durga P, additional, Agarwal, Vikas, additional, Sharma, Aditya M, additional, Viswanathan, Vijay, additional, Rathore, Vijay S, additional, Turk, Monika, additional, Kolluri, Raghu, additional, Viskovic, Klaudija, additional, Cuadrado-Godia, Elisa, additional, Kitas, George D, additional, Nambi, Vijay, additional, Bhatt, Deepak L, additional, Nicolaides, Andrew, additional, and Suri, Jasjit S, additional

Published
2022
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11. Magnetic resonance based Wilson’s disease tissue characterization in an artificial intelligence framework using transfer learning

Author

Skandha, Siva, primary, Saba, Luca, additional, Gupta, Suneet K, additional, Kumar, Vijaya K, additional, Johri, Amer M, additional, Khanna, Narendra N, additional, Mavrogeni, Sophie, additional, Laird, John R, additional, Pareek, Gyan, additional, Sfikakis, Petros P, additional, Protogerou, Athanasios, additional, Turk, Monica, additional, Sharma, Aditya M, additional, Nicolaides, Andrew, additional, Kitas, George D, additional, Viskovic, Klaudija, additional, Omerzu, Tomaz, additional, and Suri, Jasjit S, additional

Published
2022
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12. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

Author

Agarwal, Mohit, Agarwal, Sushant, Saba, Luca, Chabert, Gian Luca, Gupta, Suneet, Carriero, Alessandro, Pasche, Alessio, Danna, Pietro, Mehmedovic, Armin, Faa, Gavino, Shrivastava, Saurabh, Jain, Kanishka, Jain, Harsh, Jujaray, Tanay, Singh, Inder M., Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Khanna, Narendra N., Mavrogeni, Sophie, Laird, John R., Sobel, David W., Miner, Martin, Balestrieri, Antonella, Sfikakis, Petros P., Tsoulfas, George, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D., Teji, Jagjit S., Al-Maini, Mustafa, Dhanjil, Surinder K., Nicolaides, Andrew, Sharma, Aditya, Rathore, Vijay, Fatemi, Mostafa, Alizad, Azra, Krishnan, Pudukode R., Yadav, Rajanikant R., Nagy, Frence, Kincses, Zsigmond Tamás, Ruzsa, Zoltan, Naidu, Subbaram, Viskovic, Klaudija, Kalra, Manudeep K., and Suri, Jasjit S.

Published
2022
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13. An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review.

Author

Saba, Luca, Maindarkar, Mahesh, Khanna, Narendra N., Puvvula, Anudeep, Faa, Gavino, Isenovic, Esma, Johri, Amer, Fouda, Mostafa M., Tiwari, Ekta, Kalra, Manudeep K., and Suri, Jasjit S.

Abstract

Background: Obstructive sleep apnea (OSA) is a severe condition associated with numerous cardiovascular complications, including heart failure. The complex biological and morphological relationship between OSA and atherosclerotic cardiovascular disease (ASCVD) poses challenges in predicting adverse cardiovascular outcomes. While artificial intelligence (AI) has shown potential for predicting cardiovascular disease (CVD) and stroke risks in other conditions, there is a lack of detailed, bias-free, and compressed AI models for ASCVD and stroke risk stratification in OSA patients. This study aimed to address this gap by proposing three hypotheses: (i) a strong relationship exists between OSA and ASCVD/stroke, (ii) deep learning (DL) can stratify ASCVD/stroke risk in OSA patients using surrogate carotid imaging, and (iii) including OSA risk as a covariate with cardiovascular risk factors can improve CVD risk stratification. Methods: The study employed the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) search strategy, yielding 191 studies that link OSA with coronary, carotid, and aortic atherosclerotic vascular diseases. This research investigated the link between OSA and CVD, explored DL solutions for OSA detection, and examined the role of DL in utilizing carotid surrogate biomarkers by saving costs. Lastly, we benchmark our strategy against previous studies. Results: (i) This study found that CVD and OSA are indirectly or directly related. (ii) DL models demonstrated significant potential in improving OSA detection and proved effective in CVD risk stratification using carotid ultrasound as a biomarker. (iii) Additionally, DL was shown to be useful for CVD risk stratification in OSA patients; (iv) There are important AI attributes such as AI-bias, AI-explainability, AI-pruning, and AI-cloud, which play an important role in CVD risk for OSA patients. Conclusions: DL provides a powerful tool for CVD risk stratification in OSA patients. These results can promote several recommendations for developing unique, bias-free, and explainable AI algorithms for predicting ASCVD and stroke risks in patients with OSA. [ABSTRACT FROM AUTHOR]

Published
2024
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14. A Generalized and Robust Nonlinear Approach based on Machine Learning for Intrusion Detection.

Author

Rahman, Jakiur, Singh, Jaskaran, Nayak, Soumen, Jena, Biswajit, Mohanty, Lopamudra, Singh, Narpinder, Laird, John R., Singh, Rajesh, Garg, Deepak, Khanna, Narendra N., Fouda, Mostafa M., Saba, Luca, and Suri, Jasjit S.

Subjects
COMPUTER network security, MACHINE learning, SAMPLE size (Statistics), INTRUSION detection systems (Computer security)
Abstract

Intrusion detection systems (IDS) play a critical role in ensuring the security and integrity of computer networks. There is a constant demand for the development of powerful, novel, and generalized methods for IDS that can accurately detect and classify intrusions. In this study, we aim to evaluate the benefits of linear classifiers (LC) and nonlinear classifiers (NLC) in IDS. We employed ten machine learning (ML) classifiers, consisting of five LC and five NLC. These classifiers underwent cross-validation for performance evaluation, unseen analysis, statistical tests, and power analysis on measuring the minimum sample size. Four hypotheses were formulated and validated on five processed intrusion attack datasets. NLC outperformed LC, with a mean accuracy (ACC)/area-under-the-curve (AUC) increase of 22.26%/20.3% on the WUSTL-EHMS dataset, with improvements of ACC/AUC by 5.5%/2.3% on the UNSW-NB15 dataset. In the unseen analysis, NLC achieved an ACC/AUC increase of 21.9%/21.8% when trained on WUSTL-EHMS and tested on UNSW-NB15. Lastly, when using a mixed dataset of WUSTL-EHMS and UNSW-NB15, NLC demonstrated an ACC/AUC increase of 11.67%/5.5%. The model performed well in cross-validation protocols, and the statistical tests yielded significant p-values. NLC provides generalized and robust solutions to detect intrusion attacks, ensuring the integrity and security of computer networks. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

Author

Suri, Jasjit S., Agarwal, Sushant, Saba, Luca, Chabert, Gian Luca, Carriero, Alessandro, Paschè, Alessio, Danna, Pietro, Mehmedović, Armin, Faa, Gavino, Jujaray, Tanay, Singh, Inder M., Khanna, Narendra N., Laird, John R., Sfikakis, Petros P., Agarwal, Vikas, Teji, Jagjit S., R Yadav, Rajanikant, Nagy, Ferenc, Kincses, Zsigmond Tamás, Ruzsa, Zoltan, Viskovic, Klaudija, and Kalra, Mannudeep K.

Published
2022
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18. A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

Author

Suri, Jasjit S., Agarwal, Sushant, Gupta, Suneet K., Puvvula, Anudeep, Biswas, Mainak, Saba, Luca, Bit, Arindam, Tandel, Gopal S., Agarwal, Mohit, Patrick, Anubhav, Faa, Gavino, Singh, Inder M., Oberleitner, Ronald, Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Miguel Sanches, J., Khanna, Narendra N., Viskovic, Klaudija, Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Sobel, David W., Balestrieri, Antonella, Sfikakis, Petros P., Tsoulfas, George, Protogerou, Athanasios, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D., Ahluwalia, Puneet, Teji, Jagjit, Al-Maini, Mustafa, Dhanjil, Surinder K., Sockalingam, Meyypan, Saxena, Ajit, Nicolaides, Andrew, Sharma, Aditya, Rathore, Vijay, Ajuluchukwu, Janet N.A., Fatemi, Mostafa, Alizad, Azra, Viswanathan, Vijay, Krishnan, P.K., and Naidu, Subbaram

Published
2021
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19. A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

Author

Biswas, Mainak, Saba, Luca, Omerzu, Tomaž, Johri, Amer M., Khanna, Narendra N., Viskovic, Klaudija, Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Balestrieri, Antonella, Sfikakis, Petros P, Protogerou, Athanasios, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D, Kolluri, Raghu, Sharma, Aditya, Viswanathan, Vijay, Ruzsa, Zoltan, Nicolaides, Andrew, and Suri, Jasjit S.

Published
2021
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20. Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

Author

Bhagawati, Mrinalini, Paul, Sudip, Mantella, Laura, Johri, Amer M., Gupta, Siddharth, Laird, John R., Singh, Inder M., Khanna, Narendra N., Al-Maini, Mustafa, Isenovic, Esma R., Tiwari, Ekta, Singh, Rajesh, Nicolaides, Andrew, Saba, Luca, Anand, Vinod, and Suri, Jasjit S.

Subjects
DEEP learning, MACHINE learning, FEATURE selection, CAROTID artery ultrasonography, ATHEROSCLEROTIC plaque
Abstract

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. Methodology: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. Results: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. Conclusions: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound

Author

Jamthikar, Ankush D., Gupta, Deep, Saba, Luca, Khanna, Narendra N., Viskovic, Klaudija, Mavrogeni, Sophie, Laird, John R., Sattar, Naveed, Johri, Amer M., Pareek, Gyan, Miner, Martin, Sfikakis, Petros P., Protogerou, Athanasios, Viswanathan, Vijay, Sharma, Aditya, Kitas, George D., Nicolaides, Andrew, Kolluri, Raghu, and Suri, Jasjit S.

Published
2020
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22. 3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0

Author

Skandha, Sanagala S., Gupta, Suneet K., Saba, Luca, Koppula, Vijaya K., Johri, Amer M., Khanna, Narendra N., Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Sfikakis, Petros P., Protogerou, Athanasios, Misra, Durga P., Agarwal, Vikas, Sharma, Aditya M., Viswanathan, Vijay, Rathore, Vijay S., Turk, Monika, Kolluri, Raghu, Viskovic, Klaudija, Cuadrado-Godia, Elisa, Kitas, George D., Nicolaides, Andrew, and Suri, Jasjit S.

Published
2020
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23. COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Author

Suri, Jasjit S., Puvvula, Anudeep, Biswas, Mainak, Majhail, Misha, Saba, Luca, Faa, Gavino, Singh, Inder M., Oberleitner, Ronald, Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Sanches, J. Miguel, Khanna, Narendra N., Viskovic, Klaudija, Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Sobel, David W., Balestrieri, Antonella, Sfikakis, Petros P., Tsoulfas, George, Protogerou, Athanasios, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D., Ahluwalia, Puneet, Kolluri, Raghu, Teji, Jagjit, Maini, Mustafa Al, Agbakoba, Ann, Dhanjil, Surinder K., Sockalingam, Meyypan, Saxena, Ajit, Nicolaides, Andrew, Sharma, Aditya, Rathore, Vijay, Ajuluchukwu, Janet N.A., Fatemi, Mostafa, Alizad, Azra, Viswanathan, Vijay, Krishnan, Pudukode R., and Naidu, Subbaram

Published
2020
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25. 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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26. Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

Author

Saba, Luca, Agarwal, Mohit, Patrick, Anubhav, Puvvula, Anudeep, Gupta, Suneet K., Carriero, Alessandro, Laird, John R., Kitas, George D., Johri, Amer M., Balestrieri, Antonella, Falaschi, Zeno, Paschè, Alessio, Viswanathan, Vijay, El-Baz, Ayman, Alam, Iqbal, Jain, Abhinav, Naidu, Subbaram, Oberleitner, Ronald, Khanna, Narendra N., Bit, Arindam, Fatemi, Mostafa, Alizad, Azra, and Suri, Jasjit S.

Published
2021
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27. Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: a supercomputer application

Author

Agarwal, Mohit, Saba, Luca, Gupta, Suneet K., Johri, Amer M., Khanna, Narendra N., Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Sfikakis, Petros P., Protogerou, Athanasios, Sharma, Aditya M., Viswanathan, Vijay, Kitas, George D., Nicolaides, Andrew, and Suri, Jasjit S.

Published
2021
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29. Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach

Author

Cuadrado-Godia, Elisa, Jamthikar, Ankush D., Gupta, Deep, Khanna, Narendra N., Araki, Tadashi, Maniruzzaman, Md, Saba, Luca, Nicolaides, Andrew, Sharma, Aditya, Omerzu, Tomaz, Suri, Harman S., Gupta, Ajay, Mavrogeni, Sophie, Turk, Monika, Laird, John R., Protogerou, Athanasios, Sfikakis, Petros, Kitas, George D., Viswanathan, Vijay, and Suri, Jasjit S.

Published
2019
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30. Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study

Author

Khanna, Narendra N., Jamthikar, Ankush D., Gupta, Deep, Nicolaides, Andrew, Araki, Tadashi, Saba, Luca, Cuadrado-Godia, Elisa, Sharma, Aditya, Omerzu, Tomaz, Suri, Harman S., Gupta, Ajay, Mavrogeni, Sophie, Turk, Monika, Laird, John R., Protogerou, Athanasios, Sfikakis, Petros P., Kitas, George D., Viswanathan, Vijay, and Suri, Jasjit S.

Published
2019
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31. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Author

Kumari, Vandana, primary, Kumar, Naresh, additional, Kumar K, Sampath, additional, Kumar, Ashish, additional, Skandha, Sanagala S., additional, Saxena, Sanjay, additional, Khanna, Narendra N., additional, Laird, John R., additional, Singh, Narpinder, additional, Fouda, Mostafa M., additional, Saba, Luca, additional, Singh, Rajesh, additional, and Suri, Jasjit S., additional

Published
2023
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33. Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort

Author

Cuadrado-Godia, Elisa, Maniruzzaman, Md, Araki, Tadashi, Puvvula, Anudeep, Jahanur Rahman, Md, Saba, Luca, Suri, Harman S., Gupta, Ajay, Banchhor, Sumit K., Teji, Jagjit S., Omerzu, Tomaž, Khanna, Narendra N., Laird, John R., Nicolaides, Andrew, Mavrogeni, Sophie, Kitas, George D., and Suri, Jasjit S.

Published
2018
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37. Personalized Medicine for Cardiovascular Disease Risk in Artificial Intelligence Framework

Author

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

Published
2023
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38. DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images

Author

Sanga, Prabhav, primary, Singh, Jaskaran, additional, Dubey, Arun Kumar, additional, Khanna, Narendra N., additional, Laird, John R., additional, Faa, Gavino, additional, Singh, Inder M., additional, Tsoulfas, Georgios, additional, Kalra, Mannudeep K., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Rathore, Vijay, additional, Agarwal, Vikas, additional, Ahluwalia, Puneet, additional, Fouda, Mostafa M., additional, Saba, Luca, additional, and Suri, Jasjit S., additional

Published
2023
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39. A special report on changing trends in preventive stroke/cardiovascular risk assessment via B-mode ultrasonography

Author

Jamthikar, Ankush, primary, Gupta, Deep, additional, Khanna, Narendra N., additional, Araki, Tadashi, additional, Saba, Luca, additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Omerzu, Tomaz, additional, Suri, Harman S., additional, Gupta, Ajay, additional, Mavrogeni, Sophie, additional, Turk, Monika, additional, Laird, John R., additional, Protogerou, Athanasios, additional, Sfikakis, Petros P., additional, Kitas, George D., additional, Viswanathan, Vijay, additional, Pareek, Gyan, additional, Miner, Martin, additional, and Suri, Jasjit S., additional

Published
2020
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40. List of contributors

Author

Araki, Tadashi, primary, Bagga, Jaspal, additional, Barde, Snehlata, additional, Bharne, Ashish, additional, Borkar, Chandrashekhar, additional, Chakraborty, Pavan, additional, Choubey, Abha, additional, Choubey, Abhishek, additional, Choubey, Shruti Bhargava, additional, Choubey, Siddhartha, additional, CSN, Koushik, additional, Desai, Santosh R., additional, Dilliwar, Vikas, additional, Goyal, Shruti, additional, Guggari, Shankru, additional, Gupta, Ajay, additional, Gupta, Deep, additional, Indrajeet, Indrajeet, additional, Jamthikar, Ankush, additional, Kaimal, Veena, additional, Khanna, Narendra N., additional, Kitas, George D., additional, Koushik, C.S.N., additional, Laird, John R., additional, Mahato, Seema, additional, Mavrogeni, Sophie, additional, Miner, Martin, additional, Mishra, Jaya, additional, Miyapuram, Krishna P., additional, Momin, Abdul, additional, Nagendra, H., additional, Nakhate, Kartik, additional, Nicolaides, Andrew, additional, Omerzu, Tomaz, additional, Pareek, Gyan, additional, Protogerou, Athanasios, additional, Rahaman, Valiur, additional, Ray, Supriya, additional, Saba, Luca, additional, Sahu, Mridu, additional, Sahula, Vineet, additional, Saini, Sandeep, additional, Sanyal, Sudip, additional, Sfikakis, Petros P., additional, Shahu, Ambika, additional, Sharaff, Aakanksha, additional, Sharma, Aditya, additional, Sharma, Sanjiv, additional, Singh, Sandeep Kumar, additional, Soni, Asma, additional, Srivastava, Saurabh Kumar, additional, Suri, Harman S., additional, Suri, Jasjit S., additional, Thomas, Ani, additional, Turk, Monika, additional, Umadevi, V, additional, Viswanathan, Vijay, additional, and Yamazaki, Tatsuya, additional

Published
2020
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41. Geometric total plaque area is an equally powerful phenotype compared with carotid intima–media thickness for stroke risk assessment: A deep learning approach

Author

Cuadrado-Godia, Elisa, primary, Srivastava, Saurabh K., additional, Saba, Luca, additional, Araki, Tadashi, additional, Suri, Harman S., additional, Giannopolulos, Argiris, additional, Omerzu, Tomaz, additional, Laird, John, additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Kitas, George D., additional, Nicolaides, Andrew, additional, and Suri, Jasjit S., additional

Published
2020
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42. Deep-learning strategy for accurate carotid intima–media thickness measurement: an ultrasound study on a Japanese diabetic cohort

Author

Biswas, Mainak, primary, Kuppili, Venkatanareshbabu, additional, Araki, Tadashi, additional, Edla, Damodar Reddy, additional, Godia, Elisa Cuadrado, additional, Saba, Luca, additional, Suri, Harman S, additional, Omerzu, Tomaž, additional, Laird, John R, additional, Khanna, Narendra N, additional, Nicolai, Andrew, additional, and Suri, Jasjit S, additional

Published
2019
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43. Artificial intelligence bias in medical system designs: a systematic review

Author

Kumar, Ashish, primary, Aelgani, Vivekanand, additional, Vohra, Rubeena, additional, Gupta, Suneet K., additional, Bhagawati, Mrinalini, additional, Paul, Sudip, additional, Saba, Luca, additional, Suri, Neha, additional, Khanna, Narendra N., additional, Laird, John R., additional, Johri, Amer M., additional, Kalra, Manudeep, additional, Fouda, Mostafa M., additional, Fatemi, Mostafa, additional, Naidu, Subbaram, additional, and Suri, Jasjit S., additional

Published
2023
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44. Cardiovascular disease/stroke risk stratification in deep learning framework: a review

Author

Bhagawati, Mrinalini, primary, Paul, Sudip, additional, Agarwal, Sushant, additional, Protogeron, Athanasios, additional, Sfikakis, Petros P., additional, Kitas, George D., additional, Khanna, Narendra N., additional, Ruzsa, Zoltan, additional, Sharma, Aditya M., additional, Tomazu, Omerzu, additional, Turk, Monika, additional, Faa, Gavino, additional, Tsoulfas, George, additional, Laird, John R., additional, Rathore, Vijay, additional, Johri, Amer M., additional, Viskovic, Klaudija, additional, Kalra, Manudeep, additional, Balestrieri, Antonella, additional, Nicolaides, Andrew, additional, Singh, Inder M., additional, Chaturvedi, Seemant, additional, Paraskevas, Kosmas I., additional, Fouda, Mostafa M., additional, Saba, Luca, additional, and Suri, Jasjit S., additional

Published
2023
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45. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework

Author

Dubey, Arun Kumar, Chabert, Gian Luca, Carriero, Alessandro, Pasche, Alessio, Danna, Pietro S. C., Agarwal, Sushant, Mohanty, Lopamudra, Nillmani, Lopamudra, Sharma, Neeraj, Yadav, Sarita, Jain, Achin, Kumar, Ashish, Kalra, Mannudeep K., Sobel, David W., Laird, John R., Singh, Inder M., Singh, Narpinder, Tsoulfas, George, Fouda, Mostafa M., Alizad, Azra, Kitas, George D., Khanna, Narendra N., Viskovic, Klaudija, Kukuljan, Melita, Al-Maini, Mustafa, El-Baz, Ayman, Saba, Luca, and Suri, Jasjit S.

Subjects
ResNet–UNet, transfer learning, ensemble deep learning, BIOMEDICINA I ZDRAVSTVO. Kliničke medicinske znanosti. Radiologija, control, BIOMEDICINE AND HEALTHCARE. Clinical Medical Sciences. Radiology, COVID, unseen
Abstract

Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Published
2023

46. A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography

Author

Jamthikar, Ankush, Gupta, Deep, Khanna, Narendra N., Araki, Tadashi, Saba, Luca, Nicolaides, Andrew, Sharma, Aditya, Omerzu, Tomaz, Suri, Harman S., Gupta, Ajay, Mavrogeni, Sophie, Turk, Monika, Laird, John R., Protogerou, Athanasios, Sfikakis, Petros P., Kitas, George D., Viswanathan, Vijay, Pareek, Gyan, Miner, Martin, and Suri, Jasjit S.

Published
2019
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47. 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

48. 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

49. 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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50. UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Author

Suri, Jasjit S., primary, Bhagawati, Mrinalini, additional, Agarwal, Sushant, additional, Paul, Sudip, additional, Pandey, Amit, additional, Gupta, Suneet K., additional, Saba, Luca, additional, Paraskevas, Kosmas I., additional, Khanna, Narendra N., additional, Laird, John R., additional, Johri, Amer M., additional, Kalra, Manudeep K., additional, Fouda, Mostafa M., additional, Fatemi, Mostafa, additional, and Naidu, Subbaram, additional

Published
2023
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