Diagnostics (Jun 2022)
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review
- Jasjit S. Suri,
- Mahesh A. Maindarkar,
- Sudip Paul,
- Puneet Ahluwalia,
- Mrinalini Bhagawati,
- Luca Saba,
- Gavino Faa,
- Sanjay Saxena,
- Inder M. Singh,
- Paramjit S. Chadha,
- Monika Turk,
- Amer Johri,
- Narendra N. Khanna,
- Klaudija Viskovic,
- Sofia Mavrogeni,
- John R. Laird,
- Martin Miner,
- David W. Sobel,
- Antonella Balestrieri,
- Petros P. Sfikakis,
- George Tsoulfas,
- Athanase D. Protogerou,
- Durga Prasanna Misra,
- Vikas Agarwal,
- George D. Kitas,
- Raghu Kolluri,
- Jagjit S. Teji,
- Mustafa Al-Maini,
- Surinder K. Dhanjil,
- Meyypan Sockalingam,
- Ajit Saxena,
- Aditya Sharma,
- Vijay Rathore,
- Mostafa Fatemi,
- Azra Alizad,
- Padukode R. Krishnan,
- Tomaz Omerzu,
- Subbaram Naidu,
- Andrew Nicolaides,
- Kosmas I. Paraskevas,
- Mannudeep Kalra,
- Zoltán Ruzsa,
- Mostafa M. Fouda
Affiliations
- Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Mahesh A. Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
- Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
- Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
- Luca Saba
- Department of Radiology, and Pathology, Azienda Ospedaliero Universitaria, 09123 Cagliari, Italy
- Gavino Faa
- Department of Radiology, and Pathology, Azienda Ospedaliero Universitaria, 09123 Cagliari, Italy
- Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751029, India
- Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Paramjit S. Chadha
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Monika Turk
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
- Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
- Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
- Sofia Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 176 74 Athens, Greece
- John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
- Martin Miner
- Men’s Health Centre, Miriam Hospital, Providence, RI 02906, USA
- David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 157 72 Athens, Greece
- Antonella Balestrieri
- Docs Eye Care Research Lab, Dunedin 9013, New Zealand
- Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 157 72 Athens, Greece
- George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 541 24 Thessaloniki, Greece
- Athanase D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 157 72 Athens, Greece
- Durga Prasanna Misra
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
- Vikas Agarwal
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
- George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Raghu Kolluri
- OhioHealth Heart and Vascular, Mansfield, OH 44905, USA
- Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Mustafa Al-Maini
- Allergy, Clinical Immunology, and Rheumatology Institute, Toronto, ON M5G 1N8, Canada
- Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Meyypan Sockalingam
- MV Centre of Diabetes, Chennai 600013, India
- Ajit Saxena
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
- Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95823, USA
- Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Padukode R. Krishnan
- Neurology Department, Fortis Hospital, Bangalore 560076, India
- Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
- Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
- Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Engomi 2408, Cyprus
- Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece
- Mannudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Zoltán Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
- Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
- DOI
- https://doi.org/10.3390/diagnostics12071543
- Journal volume & issue
-
Vol. 12,
no. 7
p. 1543
Abstract
Background and Motivation: 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 cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.
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