A Comprehensive Review on Advancements in Artificial Intelligence Approaches and Future Perspectives for Early Diagnosis of Parkinson's Disease
Keywords:Parkinson's disease, Machine Learning, Artificial Intelligence, Deep Learning, Early Detection
Parkinson's disease (PD) is a neurological condition that generally strikes people in their average age of onset for PD a neurological disorder, is 55 and up. A wide variety of motor and non-motor symptoms can be observed in patients with PD. The medical community has made great strides in recent years, but Parkinson's disease still has no treatment or cure. Therefore, exploring possible ways for early PD identification is an intriguing scientific endeavor. Full symptoms may not appear for years due to the progressive nature of PD. Thus, early diagnosis is vital to enhance the patient's quality of life. Symptoms will usually worsen with time, so keep that in mind. Several neurodegenerative disorders share very similar symptoms, making early identification crucial for disease prediction. Many people are starting to pay attention to using Artificial Intelligence (AI) methods in medical diagnostics because they can process massive volumes of data and make reliable statistical predictions. This paper covers all the bases when it comes to artificial intelligence (AI) approaches to PD diagnosis, including the many deep and machine learning-based methods that have been deployed and how they have opened new avenues for research. Furthermore, the study explores the current situation and future possibilities of data-driven AI approaches to Parkinson's disease diagnosis. This study is an excellent resource as a review article for researchers interested in creating PD prediction models employing different AI-based modalities.
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Copyright (c) 2024 Aiesha Mahmoud Ibrahim, Mazin Abed Mohammed
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