Naslov
Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Autori
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. ; Višković, 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.

Izvornik
Diagnostics (2075-4418) 12 (2022), 7; 47, 47

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Parkinson’s disease ; COVID-19 ; cardiovascular/stroke risk stratification ; deep learning ; bias

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti

POVEZANOST RADA