Julian Dana R, Bahramy Afshin, Neal Makayla, Pearce Thomas M, Kofler Julia
Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania.
Am J Pathol. 2025 Feb 13. doi: 10.1016/j.ajpath.2024.12.018.
Computational neurodegenerative neuropathology represents a transformative approach in the analysis and understanding of neurodegenerative diseases through utilization of whole slide images (WSIs) 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 WSI 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 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.
计算神经退行性神经病理学是一种变革性方法，通过利用全切片图像（WSIs）和先进的机器学习/人工智能（ML/AI）技术来分析和理解神经退行性疾病。本综述探讨了计算神经退行性神经病理学这一新兴领域，强调其在加强神经病理学评估、诊断和研究方面的潜力。ML/AI技术的最新进展通过自动化疾病分期、识别新的形态学生物标志物以及通过多模态人工智能方法揭示新的临床见解，对包括解剖病理学在内的基于图像的医学领域产生了重大影响。尽管前景广阔，但该领域面临着一些挑战，包括专家注释有限、切片扫描难以获取、机构间差异以及共享大型WSI数据集的复杂性。本综述讨论了提高深度学习模型准确性和效率以更好地解释神经病理学数据的重要性。它强调了无监督学习在识别未注释数据中的模式方面的潜力。此外，可解释人工智能模型的开发对于实验神经病理学至关重要。通过应对这些挑战并利用前沿人工智能技术，计算神经退行性神经病理学有可能彻底改变该领域，并显著推进我们对疾病的理解。
