Abstract
Computational neurodegenerative neuropathology represents a transformative approach in the analysis and understanding of neurodegenerative diseases through utilization of whole slide images and advanced machine learning/artificial intelligence (ML/AI) techniques. This review explores the emerging field of computational neurodegenerative neuropathology, emphasizing its potential to enhance neuropathologic assessment, diagnosis, and research. Recent advancements in ML/AI technologies have significantly affected image-based medical fields, including anatomic pathology, by automating disease staging, identifying novel morphologic 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 whole slide image data sets. This review discusses the importance of improving deep learning model accuracy and efficiency for better interpretation of neuropathologic data. It highlights the potential of unsupervised learning to identify patterns in unannotated data. Furthermore, the development of explainable ML/AI models is crucial for experimental neuropathology. By 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.