Towards Robust Parkinson’s Disease Identification: A Multi‑Modal Deep Learning Approach

Parkinson’s Disease (PD) is a progressive neurological disorder that primarily affects movement, coordination, and motor control. Early diagnosis of Parkinson’s Disease is essential for timely treatment and improving the quality of life of patients. Traditional diagnosis methods mainly depend on clinical observation and medical expertise, which may delay early-stage detection. With the advancement of Artificial Intelligence, Deep Learning techniques have shown promising results in the automated detection of Parkinson’s Disease using medical data such as handwriting patterns, speech signals, gait analysis, MRI scans, and tremor measurements. This review paper presents an overview of various Deep Learning models used for Parkinson’s Disease detection, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid models such as CNN-LSTM. It also discusses commonly used datasets, preprocessing methods, feature extraction techniques, performance evaluation metrics, and challenges faced in implementation. Furthermore, the paper highlights recent advancements and future opportunities in real-time healthcare systems for Parkinson’s monitoring and prediction. The study concludes that Deep Learning models can significantly improve diagnostic accuracy and support healthcare professionals in early and efficient Parkinson’s Disease detection.