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. SfikakisGeorge 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

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations


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 be-come 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.

Original languageEnglish (US)
Article number1543
Issue number7
StatePublished - Jul 2022

Bibliographical note

Funding Information:
Acknowledgments: Mahesh A. Maindarkar, would like to acknowledge the Department of Science and Technology, Government of India for sponsoring the project under the scheme IMPRINT‐2 vide file no: IMP/2018/000034, Dated: 28 March 2019.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • COVID‐19
  • Parkinson’s disease
  • bias
  • cardiovascular/stroke risk stratification
  • deep learning


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