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

IF 4.7 2区 医学 Q1 PATHOLOGY
Dana R Julian, Afshin Bahramy, Makayla Neal, Thomas M Pearce, Julia Kofler
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引用次数: 0

Abstract

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.

数字神经病理学和机器学习在神经退行性疾病研究中的最新进展。
计算神经退行性神经病理学通过利用全幻灯片图像(WSI)和先进的机器学习/人工智能(ML/AI)技术,代表了分析和理解神经退行性疾病的一种变革性方法。这篇综述探讨了计算神经退行性神经病理学的新兴领域,强调了它在增强神经病理学评估、诊断和研究方面的潜力。机器学习/人工智能技术的最新进展通过自动化疾病分期、识别新的形态生物标志物以及通过多模式人工智能方法发现新的临床见解,显著影响了基于图像的医学领域,包括解剖病理学。尽管前景光明,但该领域仍面临一些挑战,包括有限的专家注释、幻灯片扫描不可访问性、机构间的可变性以及共享大型WSI数据集的复杂性。本文讨论了提高深度学习模型的准确性和效率对于更好地解释神经病理数据的重要性。它突出了在无注释数据中识别模式的无监督学习的潜力。此外,可解释的人工智能模型的发展对实验神经病理学至关重要。通过应对这些挑战并利用尖端的人工智能技术,计算神经退行性神经病理学有可能彻底改变这一领域,并显著提高我们对疾病的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.40
自引率
0.00%
发文量
178
审稿时长
30 days
期刊介绍: The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.
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