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

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.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

“…Machine learning (ML) algorithms have been developed to enhance segmentation and classification [30,[70][71][72][73]. However, these methods lack automated feature extraction.…”

“…In contrast, ML combined with deep learning (DL) provides a powerful framework capable of automatically generating features by leveraging underlying knowledge of radiological features and genetic features. It also offers an advanced training paradigm, enabling dynamic adjustment of the nonlinear relationship between risk factors and the desired outcome, making ML/DL a potent approach [30,[70][71][72][73]. Our team has conducted an extensive examination of different applications of DL and has taken measures to appropriately prepare and balance the data sets used for training and testing purposes [74][75][76].…”

“…Artificial intelligence (AI) methods utilize machine learning (ML) algorithms to analyze complex datasets of radiological, genetics, and laboratory parameters, and generate predictions [2,[28][29][30][31][32]. Figure 1 illustrates a model that utilizes AI to stratify the risk of CVD and stroke in patients having RA.…”

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

“…AI contains various combinations of technologies. The vast majority of these technologies have an immediate application in the field of medicine, even though the specific procedures and tasks with which they can help vary considerably [ 14 , 89 , 90 , 91 , 92 ]. The following list identifies and provides explanations for several essential AI technologies for the healthcare industry.…”

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

“…In this scenario, a recommender system (RS) using machine learning (ML) approaches might be used to administer the best treatment while working with limited resources [ 17 , 18 ]. As the mortality rate and recovery rate of seriously hospitalized COVID-19 patients generally depend upon the amount of infection in the lungs [ 19 , 20 , 21 ], the radiographic lung images of those patients can be used to recommend proper treatment in terms of a doctor, medicine, and other related resources.…”