Abstract
Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.
Highlights
Parkinson’s disease (PD) is a neurodegenerative disorder; James Parkinson first portrayed it in 1817 [1,2]
The model fails to explain scientific validation; it results in High-Moderate (HM) bias in the studies. (iv) For an Artificial Intelligence (AI) system to be reliable, accurate, and to have a minimal AI distortion, the bias must be minimal. (v) AI architecture such as deep layered neural network models and such as the ANN model were neglected in clinical design and decisions and indicate Moderate-Low (ML) bias in the ranking [13,62,99]
The PD risk is intensifying due to existing comorbidities with PD; it results in High-Moderate (HM) bias in the AI model
Summary
Parkinson’s disease (PD) is a neurodegenerative disorder; James Parkinson first portrayed it in 1817 [1,2]. 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. Considering the feasibility of the objective of a selection strategy (396 studies), the articles were screened. The information considered for the PD studies’ data extraction was (i) author name, (ii) year of publication, (iii) objective of the studies, (iv) demographic discussion, (v) data types, (vi) data source, (vii) diagnosis method, (viii) bias studies, and (ix) attribute studies.
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