Search Results - "Bhagawati, Mrinalini"

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

“…Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast…”
Get full text

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review by Suri, Jasjit S, Bhagawati, Mrinalini, Paul, Sudip, Protogerou, Athanasios D, Sfikakis, Petros P, Kitas, George D, Khanna, Narendra N, Ruzsa, Zoltan, Sharma, Aditya M, Saxena, Sanjay, Faa, Gavino, Laird, John R, Johri, Amer M, Kalra, Manudeep K, Paraskevas, Kosmas I, Saba, Luca

“…Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital…”
Get full text

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data by 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, Suri, Jasjit S

“…The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0…”
Get full text

A Review on Recent Advances of Cerebral Palsy by Paul, Sudip, Nahar, Anjuman, Bhagawati, Mrinalini, Kunwar, Ajaya Jang

“…This narrative review summarizes the latest advances in cerebral palsy and identifies where more research is required. Several studies on cerebral palsy were…”
Get full text

Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review by Suri, Jasjit S., Bhagawati, Mrinalini, Paul, Sudip, Protogeron, Athanasios, Sfikakis, Petros P., Kitas, George D., Khanna, Narendra N., Ruzsa, Zoltan, Sharma, Aditya M., Saxena, Sanjay, Faa, Gavino, Paraskevas, Kosmas I., Laird, John R., Johri, Amer M., Saba, Luca, Kalra, Manudeep

“…Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD)…”
Get full text

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

“…: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in…”
Get full text

Artificial intelligence bias in medical system designs: a systematic review by Kumar, Ashish, Aelgani, Vivekanand, Vohra, Rubeena, Gupta, Suneet K., Bhagawati, Mrinalini, Paul, Sudip, Saba, Luca, Suri, Neha, Khanna, Narendra N., Laird, John R., Johri, Amer M., Kalra, Manudeep, Fouda, Mostafa M., Fatemi, Mostafa, Naidu, Subbaram, Suri, Jasjit S.

“…Inherent bias in the artificial intelligence (AI)-model brings inaccuracies and variabilities during clinical deployment of the model. It is challenging to…”
Get full text

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort by 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, Suri, Jasjit S.

“…The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD)…”
Get full text

Generative Adversarial Network-based Deep Learning Framework for Cardiovascular Disease Risk Prediction by Bhagawati, Mrinalini, Paul, Sudip

“…Early risk assessment is essential since cardiovascular disease (CVD) is a significant healthcare burden. Earlier assessment methods either employed machine…”
Get full text

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study by Khanna, Narendra N, Maindarkar, Mahesh A, Viswanathan, Vijay, Puvvula, Anudeep, Paul, Sudip, Bhagawati, Mrinalini, Ahluwalia, Puneet, Ruzsa, Zoltan, Sharma, Aditya, Kolluri, Raghu, Krishnan, Padukone R, Singh, Inder M, Laird, John R, Fatemi, Mostafa, Alizad, Azra, Dhanjil, Surinder K, Saba, Luca, Balestrieri, Antonella, Faa, Gavino, Paraskevas, Kosmas I, Misra, Durga Prasanna, Agarwal, Vikas, Sharma, Aman, Teji, Jagjit S, Al-Maini, Mustafa, Nicolaides, Andrew, Rathore, Vijay, Naidu, Subbaram, Liblik, Kiera, Johri, Amer M, Turk, Monika, Sobel, David W, Miner, Martin, Viskovic, Klaudija, Tsoulfas, George, Protogerou, Athanasios D, Mavrogeni, Sophie, Kitas, George D, Fouda, Mostafa M, Kalra, Mannudeep K, Suri, Jasjit S

“…A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML)…”
Get full text

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report by Khanna, Narendra N, Maindarkar, Mahesh, Puvvula, Anudeep, Paul, Sudip, Bhagawati, Mrinalini, Ahluwalia, Puneet, Ruzsa, Zoltan, Sharma, Aditya, Munjral, Smiksha, Kolluri, Raghu, Krishnan, Padukone R, Singh, Inder M, Laird, John R, Fatemi, Mostafa, Alizad, Azra, Dhanjil, Surinder K, Saba, Luca, Balestrieri, Antonella, Faa, Gavino, Paraskevas, Kosmas I, Misra, Durga Prasanna, Agarwal, Vikas, Sharma, Aman, Teji, Jagjit, Al-Maini, Mustafa, Nicolaides, Andrew, Rathore, Vijay, Naidu, Subbaram, Liblik, Kiera, Johri, Amer M, Turk, Monika, Sobel, David W, Pareek, Gyan, Miner, Martin, Viskovic, Klaudija, Tsoulfas, George, Protogerou, Athanasios D, Mavrogeni, Sophie, Kitas, George D, Fouda, Mostafa M, Kalra, Manudeep K, Suri, Jasjit S

“…The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is…”
Get full text

Cardiovascular disease/stroke risk stratification in deep learning framework: a review by Bhagawati, Mrinalini, Paul, Sudip, Agarwal, Sushant, Protogeron, Athanasios, Sfikakis, Petros P., Kitas, George D., Khanna, Narendra N., Ruzsa, Zoltan, Sharma, Aditya M., Tomazu, Omerzu, Turk, Monika, Faa, Gavino, Tsoulfas, George, Laird, John R., Rathore, Vijay, Johri, Amer M., Viskovic, Klaudija, Kalra, Manudeep, Balestrieri, Antonella, Nicolaides, Andrew, Singh, Inder M., Chaturvedi, Seemant, Paraskevas, Kosmas I., Fouda, Mostafa M., Saba, Luca, Suri, Jasjit S.

“…The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive…”
Get full text

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review by Suri, Jasjit S, Maindarkar, Mahesh A, Paul, Sudip, Ahluwalia, Puneet, Bhagawati, Mrinalini, Saba, Luca, Faa, Gavino, Saxena, Sanjay, Singh, Inder M, Chadha, Paramjit S, Turk, Monika, Johri, Amer, Khanna, Narendra N, Viskovic, Klaudija, Mavrogeni, Sofia, Laird, John R, Miner, Martin, Sobel, David W, Balestrieri, Antonella, Sfikakis, Petros P, Tsoulfas, George, Protogerou, Athanase D, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D, Kolluri, Raghu, Teji, Jagjit S, Al-Maini, Mustafa, Dhanjil, Surinder K, Sockalingam, Meyypan, Saxena, Ajit, Sharma, Aditya, Rathore, Vijay, Fatemi, Mostafa, Alizad, Azra, Krishnan, Padukode R, Omerzu, Tomaz, Naidu, Subbaram, Nicolaides, Andrew, Paraskevas, Kosmas I, Kalra, Mannudeep, Ruzsa, Zoltán, Fouda, Mostafa M

“…: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict…”
Get full text

Machine Learning for Major Depressive Disorder: An Overview by Bhagawati, Mrinalini, Paul, Sudip

“…Major Depressive Disorder (MDD) is very much pervasive in the present period. MDD is when an episode of discrete behavior sustains for at least two weeks. It…”
Get full text

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

“…Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast…”
Get full text