Artificial Intelligence in the Diagnosis of Parkinson's Disease

Review Article

Abstract

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder where the loss of neurons and synaptic dysfunction can be seen. The alpha-synuclein present in the filament of Lewy body gets mutated where it is connected to familial PD. The gait position, tremors, tone, Stiffness and handwriting will be seen as symptoms. Based on these, biomarkers have been used but those are late and costly in clinical practice to identify quickly weather symptoms related to PD. AI, identifies risk assessment before clinical diagnosis by the help of radio waves, breath belt data set in the natural breathing signals. Nanorobots are also introduced in the identification with help of AI and ML. This gives accurate results. The signal transmission curve will be obtained then the range in between 0.8 to 0.9 indicates the diagnosis as PD. The integration of AI algorithms in PD research has particularly shown promise in enhancing diagnostic accuracy, predicting disease progression, and personalizing treatment plans based on individual patient profiles. There are some disadvantages like data privacy and security, algorithm bias, Integration with clinical practice, cost and accessibility. However, there are several advantages like early diagnosis, Research and drug development, personalized treatment, monitoring and management. Artificial intelligence holds and plays a vital role in the future, such as Early diagnosis personalized treatment plan, continue monitoring, Remote case telemedicine, predictive analytics, integration with other technologies are capabilities of artificial intelligence improve the outcome and quality of the life for individual living with better treatment strategies