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27 results on '"Jamthikar, Ankush D."'

5. CNN and RF Based Classification of Brain Tumors in MR Neurological Images

Author

Saraswathi, Vishlavath, Jamthikar, Ankush D., Gupta, Deep, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nain, Neeta, editor, Vipparthi, Santosh Kumar, editor, and Raman, Balasubramanian, editor

Published
2020
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7. 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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9. 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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10. 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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17. Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review

Author

Viswanathan, Vijay, primary, Puvvula, Anudeep, additional, Jamthikar, Ankush D, additional, Saba, Luca, additional, Johri, Amer M, additional, Kotsis, Vasilios, additional, Khanna, Narendra N, additional, Dhanjil, Surinder K, additional, Majhail, Misha, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D, additional, Sharma, Aditya M, additional, Kolluri, Raghu, additional, Naidu, Subbaram, additional, and Suri, Jasjit S, additional

Published
2021
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18. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review

Author

JAMTHIKAR, Ankush D., primary, PUVVULA, Anudeep, additional, GUPTA, Deep, additional, JOHRI, Amer M., additional, NAMBI, Vijay, additional, KHANNA, Narendra N., additional, SABA, Luca, additional, MAVROGENI, Sophie, additional, LAIRD, John R., additional, PAREEK, Gyan, additional, MINER, Martin, additional, SFIKAKIS, Petros P., additional, PROTOGEROU, Athanasios, additional, KITAS, George D., additional, NICOLAIDES, Andrew, additional, SHARMA, Aditya M., additional, VISWANATHAN, Vijay, additional, RATHORE, Vijay S., additional, KOLLURI, Raghu, additional, BHATT, Deepak L., additional, and SURI, Jasjit S., additional

Published
2021
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20. Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Author

Suri, Jasjit S. Puvvula, Anudeep Majhail, Misha Biswas, Mainak Jamthikar, Ankush D. Saba, Luca Faa, Gavino and Singh, Inder M. Oberleitner, Ronald Turk, Monika Srivastava, Saurabh Chadha, Paramjit S. Suri, Harman S. Johri, Amer M. and Nambi, Vijay Sanches, J. Miguel Khanna, Narendra N. and Viskovic, Klaudija Mavrogeni, Sophie Laird, John R. Bit, Arindam Pareek, Gyan Miner, Martin Balestrien, Antonella and Sfikakis, Petros P. Tsoulfas, George Protogerou, Athanasios and Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. and Kolluri, Raghu Teji, Jagjit Porcu, Michele Al-Maini, Mustafa and Agbakoba, Ann Sockalingam, Meyypan Sexena, Ajit and Nicolaides, Andrew Sharma, Aditya Rathore, Vijay and Viswanathan, Vijay Naidu, Subbaram Bhatt, Deepak L.

Abstract

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic “cognitive” functions that we associate with our mind, such as “learning” and “solving problem”. New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Published
2020

21. Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease

Author

Viswanathan, Vijay, primary, Jamthikar, Ankush D., additional, Gupta, Deep, additional, Puvvula, Anudeep, additional, Khanna, Narendra N., additional, Saba, Luca, additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Turk, Monika, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Ajuluchukwu, Jna, additional, Sfikakis, Petros P., additional, Protogerou, Athanasios, additional, Kitas, George D., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, and Suri, Jasjit S., additional

Published
2020
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22. Does the Carotid Bulb Offer a Better 10-Year CVD/Stroke Risk Assessment Compared to the Common Carotid Artery? A 1516 Ultrasound Scan Study

Author

Viswanathan, Vijay, primary, Jamthikar, Ankush D., additional, Gupta, Deep, additional, Puvvula, Anudeep, additional, Khanna, Narendra N., additional, Saba, Luca, additional, Viskovic, Klaudija, additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sfikakis, Petros P., additional, Protogerou, Athanasios, additional, Sharma, Aditya, additional, Kancharana, Priyanka, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Nicolaides, Andrew, additional, and Suri, Jasjit S., additional

Published
2020
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23. Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease

Author

Puvvula, Anudeep, primary, Jamthikar, Ankush D., additional, Gupta, Deep, additional, Khanna, Narendra N., additional, Porcu, Michele, additional, Saba, Luca, additional, Viskovic, Klaudija, additional, Ajuluchukwu, Janet N. A., additional, Gupta, Ajay, additional, Mavrogeni, Sophie, additional, Turk, Monika, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sfikakis, Petros P., additional, Protogerou, Athanasios, additional, Kitas, George D., additional, Nicolaides, Andrew, additional, Viswanathan, Vijay, additional, and Suri, Jasjit S., additional

Published
2020
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24. Nonlinear model for the carotid artery disease 10‐year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study

Author

Khanna, Narendra N., primary, Jamthikar, Ankush D., additional, Araki, Tadashi, additional, Gupta, Deep, additional, Piga, Matteo, additional, Saba, Luca, additional, Carcassi, Carlo, additional, Nicolaides, Andrew, additional, Laird, John R., additional, Suri, Harman S., additional, Gupta, Ajay, additional, Mavrogeni, Sophie, additional, Kitas, George D., additional, and Suri, Jasjit S., additional

Published
2019
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25. Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Author

Suri, Jasjit S., Puvvula, Anudeep, Majhail, Misha, Biswas, Mainak, Jamthikar, Ankush D., Saba, Luca, Faa, Gavino, Singh, Inder M., Oberleitner, Ronald, Turk, Monika, Srivastava, Saurabh, Chadha, Paramjit S., Suri, Harman S., Johri, Amer M., Nambi, Vijay, Sanches, J. Miguel, Khanna, Narendra N., Viskovic, Klaudija, Mavrogeni, Sophie, and Laird, John R.

Abstract

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors. [ABSTRACT FROM AUTHOR]

Published
2020
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26. Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

Author

Munjral S, Ahluwalia P, Jamthikar AD, Puvvula A, Saba L, Faa G, Singh IM, Chadha PS, Turk M, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou A, Misra P, Agarwal V, Kitas GD, Kolluri R, Teji J, Al-Maini M, Dhanjil SK, Sockalingam M, Saxena A, Sharma A, Rathore V, Fatemi M, Alizad A, Viswanathan V, Krishnan PK, Omerzu T, Naidu S, Nicolaides A, and Suri JS

Subjects
Algorithms, COVID-19 diagnostic imaging, COVID-19 virology, Humans, Risk Factors, SARS-CoV-2 isolation & purification, Arteries diagnostic imaging, Atherosclerosis diagnostic imaging, COVID-19 physiopathology, Cardiovascular Diseases diagnostic imaging, Nutritional Status
Abstract

Background : Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout : The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions : By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment., (© 2021 The Author(s). Published by BRI.)

Published
2021
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27. Low-cost preventive screening using carotid ultrasound in patients with diabetes.

Author

Viswanathan V, Jamthikar AD, Gupta D, Shanu N, Puvvula A, Khanna NN, Saba L, Omerzum T, Viskovic K, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Kitas GD, S C, Joshi S, Fiscian H, Folson AA, Wu DH, Ruzsa Z, Nicolaides A, Sharma A, Bhatt DL, and Suri JS

Subjects
Cardiovascular Diseases diagnostic imaging, Cardiovascular Diseases prevention & control, Cost-Benefit Analysis, Humans, Preventive Medicine economics, Risk Assessment economics, Risk Assessment methods, Risk Factors, Stroke diagnostic imaging, Stroke prevention & control, Ultrasonography economics, Carotid Arteries diagnostic imaging, Diabetes Complications diagnostic imaging, Diabetes Complications prevention & control, Preventive Medicine methods, Ultrasonography methods
Abstract

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high-moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image-based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment.

Published
2020
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