AI-based Parkinson's disease subtype classification using label-free Images of hiPSC-derived neurons

Abstract

Parkinson’s Disease (PD) is a complex and heterogeneous neurodegenerative disorder that poses significant challenges for personalized medicine due to its diverse pathological mechanisms and predominantly idiopathic nature. Current treatment approaches fail to address the underlying mechanisms of the disease, partly due to the lack of robust systems for identifying patient-specific mechanisms. In this study, we propose a machine learning-based classifier for PD subtypes, leveraging in vitro cellular models and a label-free imaging technique. By introducing chemically induced perturbations in healthy cortical neurons, we generated reproducible and versatile models of key PD pathological features, including mitochondrial dysfunction, lysosomal dysfunction, and protein aggregation. The transformer-based model achieved an exceptional classification accuracy of 96% during training with these chemical models. Furthermore, validation using patient-derived neurons carrying the SNCA triplication mutation underscores its translational relevance and potential for real-world applications. We establish a powerful drug screening paradigm that enables highly efficient, patient-specific therapeutic development by distinguishing the cellular and molecular underpinnings of the disease in individual patients.