Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey

• URL: http://arxiv.org/abs/2502.10703v1
• Date: Sat, 15 Feb 2025 07:26:52 GMT
• Title: Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey
• Authors: Aite Zhao, Yongcan Liu, Xinglin Yu, Xinyue Xing,
• Abstract summary: Currently, there are no effective biomarkers for diagnosing Parkinson's disease, assessing its severity, or tracking its progression. Numerous AI algorithms are now being used for PD diagnosis and treatment, capable of performing various classification tasks based on multimodal and heterogeneous disease symptom data. They provide expressive feedback, including predicting the potential likelihood of PD, assessing the severity of individual or multiple symptoms, aiding in early detection, and evaluating rehabilitation and treatment effectiveness.
• Score: 2.06242362470764
• License:
• Abstract: The rapid emergence of highly adaptable and reusable artificial intelligence (AI) models is set to revolutionize the medical field, particularly in the diagnosis and management of Parkinson's disease (PD). Currently, there are no effective biomarkers for diagnosing PD, assessing its severity, or tracking its progression. Numerous AI algorithms are now being used for PD diagnosis and treatment, capable of performing various classification tasks based on multimodal and heterogeneous disease symptom data, such as gait, hand movements, and speech patterns of PD patients. They provide expressive feedback, including predicting the potential likelihood of PD, assessing the severity of individual or multiple symptoms, aiding in early detection, and evaluating rehabilitation and treatment effectiveness, thereby demonstrating advanced medical diagnostic capabilities. Therefore, this work provides a surveyed compilation of recent works regarding PD detection and assessment through biometric symptom recognition with a focus on machine learning and deep learning approaches, emphasizing their benefits, and exposing their weaknesses, and their impact in opening up newer research avenues. Additionally, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints. Furthermore, the paper explores the potential opportunities and challenges presented by data-driven AI technologies in the diagnosis of PD.

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