TY - JOUR
T1 - Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19
T2 - A Narrative Review
AU - Suri, Jasjit S.
AU - Maindarkar, Mahesh A.
AU - Paul, Sudip
AU - Ahluwalia, Puneet
AU - Bhagawati, Mrinalini
AU - Saba, Luca
AU - Faa, Gavino
AU - Saxena, Sanjay
AU - Singh, Inder M.
AU - Chadha, Paramjit S.
AU - Turk, Monika
AU - Johri, Amer
AU - Khanna, Narendra N.
AU - Viskovic, Klaudija
AU - Mavrogeni, Sofia
AU - Laird, John R.
AU - Miner, Martin
AU - Sobel, David W.
AU - Balestrieri, Antonella
AU - Sfikakis, Petros P.
AU - Tsoulfas, George
AU - Protogerou, Athanase D.
AU - Misra, Durga Prasanna
AU - Agarwal, Vikas
AU - Kitas, George D.
AU - Kolluri, Raghu
AU - Teji, Jagjit S.
AU - Al‐maini, Mustafa
AU - Dhanjil, Surinder K.
AU - Sockalingam, Meyypan
AU - Saxena, Ajit
AU - Sharma, Aditya
AU - Rathore, Vijay
AU - Fatemi, Mostafa
AU - Alizad, Azra
AU - Krishnan, Padukode R.
AU - Omerzu, Tomaz
AU - Naidu, Subbaram
AU - Nicolaides, Andrew
AU - Paraskevas, Kosmas I.
AU - Kalra, Mannudeep
AU - Ruzsa, Zoltán
AU - Fouda, Mostafa M.
N1 - 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.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - COVID‐19
KW - Parkinson’s disease
KW - bias
KW - cardiovascular/stroke risk stratification
KW - deep learning
UR - http://www.scopus.com/inward/record.url?scp=85133265562&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133265562&partnerID=8YFLogxK
U2 - 10.3390/diagnostics12071543
DO - 10.3390/diagnostics12071543
M3 - Review article
C2 - 35885449
AN - SCOPUS:85133265562
SN - 2075-4418
VL - 12
JO - Diagnostics
JF - Diagnostics
IS - 7
M1 - 1543
ER -