Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.

SenthilarumugamVeilukandammal, Mahalakshmi; Nilakanta, Sree; Ganapathysubramanian, Baskar; Anantharam, Vellareddy; Kanthasamy, Anumantha G.; Willette, Auriel A.

doi:10.24251/hicss.2018.352

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Body posture detection is also essential because automated systems only based on their base frequency have difficulties distinguishing the two. Motor assessment of PD is the evaluation of tremors combined with bradykinesia, as well as evaluations of tremors in the mouth, jaw, bottom lip, arm, or leg [143].…”

Section: Tremormentioning

confidence: 99%

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A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Dixit

Bohre²,

Singh

et al. 2023

Electronics

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Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study.

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Section: Tremormentioning

confidence: 99%

“…Recognizing the significance of each characteristic collected through PD identification and therapy is critical, and the research serves to emphasize the data's reliability difficulties and ways to address them. We can expand their study in the future by allowing them to add more qualities and identify their involvement in PD [143].…”

mentioning

confidence: 99%