Deep learning in Parkinson’s disease

Abstract

Parkinson's disease (PD) is a slowly progressive neurodegenerative disorder, marked by both motor and non-motor symptoms, and it remains tough to diagnose and manage effectively. In recent years, deep learning (DL) has shifted the way researchers and clinicians approach PD, offering tools for automated diagnosis, tracking disease changes, and personalizing treatment plans. This chapter takes a close look at the current DL applications in PD, focusing on early detection methods that rely on neuroimaging, wearable devices, and even voice-based analysis, along with models designed to predict how the disease might progress over time. Architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and emerging transformer models are discussed, particularly in terms of their performance on PD datasets. We also explore how DL is being applied for therapy optimization, including tuning deep brain stimulation (DBS) parameters and predicting drug response patterns. Combining different types of data ranging from imaging to clinical records and genetic profiles is emphasized as a way to create models that are both stronger and easier to interpret. Finally, the chapter addresses major hurdles such as generalization issues, model transparency, and regulatory challenges, while underlining how DL can complement clinical judgment by enabling early interventions, consistent symptom tracking, and a more data-driven approach to precision medicine. Copyright © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.