Deep Learning for Parkinson’s Disease Identification: A Comprehensive Survey of Modalities, Architectures, and Emerging Trends

Parkinson Disease (PD) refers to a progressive neurodegenerative condition that acutely affects motor and non-motor functions, and the condition is, therefore, essential to diagnose and manage at an early age. In this paper, a thorough overview of deep learning methods used to detect PD will be provided, as well as a variety of modalities of data, including medical imaging (MRI, PET), speech-based data, handwriting dynamics, wearable sensor data, and clinical data. The performance, robustness, and applicability of different deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, and hybrid models are critically examined. The review identifies the role of multimodal strategies and transfer learning approaches in improving the accuracy of diagnosis and generalization. There are also problems related to limited data availability, uneven distribution of classes, lack of transparency in models, and variations in how data is gathered, which are all explained .The paper also explores emerging approaches like explainable AI, federated learning, and real-time monitoring using IoT devices, which are presented as future directions.