The Role of Neural Network for the Detection of Parkinson’s Disease: A Scoping Review

Abstract

Background: Parkinson’s Disease (PD) is a chronic neurodegenerative disorder that has been ranked second after Alzheimer’s disease worldwide. Early diagnosis of PD is crucial to combat against PD to allow patients to deal with it properly. However, there is no medical test(s) available to diagnose PD conclusively. Therefore, computer-aided diagnosis (CAD) systems offered a better solution to make the necessary data-driven decisions and assist the physician. Numerous studies were conducted to propose CAD to diagnose PD in the early stages. No comprehensive reviews have been conducted to summarize the role of AI tools to combat PD. Objective: The study aimed to explore and summarize the applications of neural networks to diagnose PD. Methods: PRISMA Extension for Scoping Reviews (PRISMA-ScR) was followed to conduct this scoping review. To identify the relevant studies, both medical databases (e.g., PubMed) and technical databases (IEEE) were searched. Three reviewers carried out the study selection and extracted the data from the included studies independently. Then, the narrative approach was adopted to synthesis the extracted data. Results: Out of 1061 studies, 91 studies satisfied the eligibility criteria in this review. About half of the included studies have implemented artificial neural networks to diagnose PD. Numerous studies included focused on the freezing of gait (FoG). Biomedical voice and signal datasets were the most commonly used data types to develop and validate these models. However, MRI- and CT-scan images were also utilized in the included studies. Conclusion: Neural networks play an integral and substantial role in combating PD. Many possible applications of neural networks were identified in this review, however, most of them are limited up to research purposes.

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

“…The research articles selected for the studies consist of various parameters like detections of the PD by using machine learning, deep learning, hybrid learning, and AI. These research articles have also shown the classification of the normal vs. PD-affected people, the demographic analysis of the PD-affected patients, and the classification of the PD by considering the input parameter alternative assessment as one method to detect PD [ 9 ]. Studies unrelated to the symptomatic observation of PD are eliminated in published papers for many reasons [ 11 , 12 , 38 ].…”

“…It is seen that many of the AI models show high accuracy, but the data size used for the testing and training of the algorithm is small, and the model fails to explain scientific validation. Hence, it results in High-Moderate (HM) in the studies [ 1 , 5 , 9 , 37 , 62 , 65 ]. The cumulative cutoff for the studies was determined by using various factors such as (i) associated studies of the PD, (ii) impact factor, (iii) the selected data, (iv) performance indicators, (v) clinical trials, etc.…”

“…The Low-Moderate (LM) studies [ 1 , 5 , 9 , 37 , 62 , 65 ] observations are the articles containing information such as (i) high data count of the PD vs. normal; (ii) performance measures; (iii) comparative analysis with various ML, DL, and HDL algorithms; (iv) explanations of the benchmarking studies. The Moderate-bias studies [ 1 , 5 , 9 , 37 , 62 , 65 , 66 ] observations were (i) sufficient data, (ii) average impact factor, and (iii) comparison of the input parameters.…”

“…PD illness is described by the disturbing dopaminergic cycle of the nerve cells of substantianigra [ 9 , 10 , 11 ]. A piece of the mind can create neurotransmitters such as “dopamine,” which fills in as a synapse for controlling developments in various body segments.…”

“…Machine learning (ML) has further enhanced the ability to accurately and swiftly make the decisions in the diagnosis of several diseases such as diabetes [ 23 , 24 ], stroke [ 25 , 26 , 27 ], coronary artery disease prediction [ 28 ], and cancer detection in the thyroid [ 29 , 30 ] liver [ 31 ], prostate [ 32 , 33 ], and ovaries [ 34 , 35 ]. Recently, there have been attempts to diagnose PD early using AI, especially using ML and DL algorithms [ 9 , 11 , 18 , 36 , 37 ]. The ML/DL algorithms are sensitive to the sample size during the training model generation, and further, due to lack of (i) scientific validation, (ii) clinical evaluation of these AI strategies, and (iii) big data configuration [ 36 ], leads to bias in the AI.…”