人工智能在神经病理学中的应用：基于深度学习的tau蛋白病变评估.docx

Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD)

and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological

phenotypes that obscure classification and quantitative assessments. Recently, powerful machine

learning-based approaches have emerged, allowing the recognition and quantification of

pathological changes from digital images. Here, we applied deep learning to the neuropathological

assessment of NFT in postmortem human brain tissue to develop a classifier capable of

recognizing and quantifying tau burden. The histopathological material was derived from 22

autopsy brains from patients with tauopathies. We used a custom web-based informatics platform

integrated with an in-house information management system to manage whole slide images (WSI)

and human expert annotations as ground truth. We utilized fully annotated regions to train a deep

per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured

performance on test data of 50 pre-annotated regions on eight naive WSI across various

tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively.

Machine learning is a useful tool for complex pathological assessment of AD and other

tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-

specific annotations with clinical, genetic, and molecular data, providing unbiased data for

clinicopathological correlations that will enhance our knowledge of the neurodegeneration.