AI Article Synopsis

  • Parkinson's disease (PD) is serious and costly to treat, and recent advancements in machine learning (ML) can predict cardiovascular and stroke risks in PD patients, but challenges arise due to COVID-19's impact on these models.
  • The study explores the hypothesis that COVID-19 exacerbates heart and brain damage in PD patients and proposes a deep learning (DL) model that factors in COVID-19 lung damage, alongside various medical data, for better risk stratification.
  • Validation of the DL model demonstrated its effectiveness in stratifying cardiovascular/stroke risk in PD patients during the pandemic, while also addressing potential biases in artificial intelligence applications for early detection of these risks.

Article Abstract

: 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. : 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. : 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. : The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324237PMC
http://dx.doi.org/10.3390/diagnostics12071543DOI Listing

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