Health AI Central
Peer-Reviewed Publication
Brain Pathol2026;36(6):e70102.November 1, 2026Journal Article

Data-driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology.

Ryan K Shahidehpour1,2, Allison M Neltner1, Mitchell A Klusty3, Cole Corbett4, Angelique D Gonzalez4, David A Gutman5, David W Fardo1, Adam D Bachstetter1,2,6, Cody Bumgardner7, Margaret E Flanagan4,8, Peter T Nelson1,7
1Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA.
2Department of Neuroscience, University of Kentucky, Lexington, Kentucky, USA.
3Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA.
4Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
5Department of Pathology, Emory University School of Medicine, Atlanta, Georgia, USA.
6Spinal Cord and Brain Injury Research Center, University of Kentucky, Lexington, Kentucky, USA.
7Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky, USA.
8Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
PubMed DOI

Abstract

Alzheimer's disease neuropathological changes (ADNC)-operationalized with semi-quantitative parameters-represent the consensus-based gold standard for diagnostic evaluation of disease severity. Although useful, ADNC diagnostic frameworks have limitations, particularly in advanced disease stages where pathological severity varies widely within a given diagnostic category. Further, some individuals…

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