Current Advancements in Digital Neuropathology and Machine Learning for the Study of Neurodegenerative Diseases.

Abstract

Computational neurodegenerative neuropathology represents a transformative approach in the analysis and understanding of neurodegenerative diseases through the utilization of whole slide images (WSI) and advanced machine learning/artificial intelligence (ML/AI) techniques. This review explores the emerging field of computational neurodegenerative neuropathology, emphasizing its potential to enhance neuropathological assessment, diagnosis, and research. Recent advancements in ML/AI technologies have significantly impacted image-based medical fields, including anatomic pathology, by automating disease staging, identifying novel morphological biomarkers, and uncovering new clinical insights via multi-modal AI approaches. Despite its promise, the field faces several challenges, including limited expert annotations, slide scanning inaccessibility, inter-institutional variability, and the complexities of sharing large WSI datasets. This review discusses the importance of improving deep learning model accuracy and efficiency for better interpretation of neuropathological data. It highlights the potential of unsupervised learning to identify patterns in unannotated data. Furthermore, the development of explainable AI models is crucial for experimental neuropathology. Through addressing these challenges and leveraging cutting-edge AI techniques, computational neurodegenerative neuropathology has the potential to revolutionize the field and significantly advance our understanding of disease.