Parkinson Disease

Parkinson's Disease (PD) research focuses on improving early diagnosis and monitoring of disease progression through the analysis of diverse data modalities, including speech, handwriting, gait, EEG, and MRI. Current studies employ various machine learning techniques, such as deep convolutional neural networks, recurrent neural networks (like LSTMs), graph neural networks, and transformer architectures, often combined with ensemble methods to enhance accuracy and robustness. These advancements aim to create accessible and reliable diagnostic tools, potentially improving patient outcomes through earlier intervention and personalized treatment strategies, particularly in resource-limited settings. The development of explainable AI models is also a key focus, facilitating clinical understanding and trust in these technologies.