Parkinson's Disease Detection Using Voice and Spiral Drawing Dataset

Rao, Korakanchi Madhu Mohan; Reddy, Mallavarapu Sai Naveen; Teja, Vemula Ravi; Krishnan, Padmaveni; Aravindhar, D. John; Sambath, M.

doi:10.1109/icssit48917.2020.9214276

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Out of a total of 29 studies, 9 studies (33%) used voice as input parameter for the detection of the PD, 5 studies (19%) used tremor data, 4 studies (15%) used sketch as the input parameter, 2 studies (7%) used EEG, and 2 studies (7%) uses telemedicine for diagnosis. From the studies, it is fair that the input parameter is the crucial factor for diagnosing the disease [ 3 , 52 , 53 , 54 ]. Figure 6 a indicates the various distributions of the input dataset features for the diagnosis of PD.…”

Section: Search Strategy and Statistical Distributionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Search Strategy and Statistical Distributionmentioning

confidence: 99%

“…The cause of the disease is currently linked with a mix of environmental factors and genetic mutations. Several hereditary variables have been demonstrated to enhance a person’s risk of getting PD, while it is unidentified how these factors make certain people more susceptible to the disease [ 53 , 55 ].…”

Section: Biology Of Parkinson’s Diseasementioning

confidence: 99%

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

et al. 2022

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“…When these neurons die or are harmed, there is less dopamine in the brain. This suggests that the area of the brain responsible for controlling movement is dysfunctional, which causes sluggish, undesired, and erratic motions of the physical parts [ 81 ]. Nerve cell degeneration happens gradually.…”

Section: Parkinson’s Disease: An Overviewmentioning

confidence: 99%

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

et al. 2022

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According to the World Health Organization (WHO), Parkinson’s disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer’s disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis ofthe use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson’s research.

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Machine Learning Based Parkinson’s Disease Detection Using Voice and Handwriting Analysis

Singh,

Kadwey,

Srivatsava

et al. 2024

Transactions on Computer Systems and Networks

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Parkinson's Disease Detection Using Voice and Spiral Drawing Dataset

Cited by 5 publications

References 9 publications

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

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

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

Machine Learning Based Parkinson’s Disease Detection Using Voice and Handwriting Analysis

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