Abstract Details

Title Diagnosis of Alzheimer’s Disease and Tauopathies on Whole Slide Images using Deep Learning Algorithm

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) S39 - Neurobiology of Dementia (3:30 PM-3:42 PM)

Poster/Presentation
Number 001

Background Neuropathological assessment at autopsy is the gold standard for diagnosing neurodegenerative disorders; however, it is time-consuming and depends on the expertise of pathologists. As digital pathology has become widely used, machine learning has been adapted for high-throughput evaluation and diagnosis of histopathologic images.

Objective To develop a pipeline for diagnosing Alzheimer's disease (AD), corticobasal degeneration (CBD), globular glial tauopathy (GGT), Pick's disease (PiD) and progressive supranuclear palsy (PSP), on a single whole slide image (WSI) of tau immunohistochemistry.

Design/Methods We used clustering-constrained-attention multiple instance learning (CLAM) on WSIs of patients with AD (n=30), CBD (n=20), GGT (n=10), PiD (n=20), PSP (n=20) and non-tauopathy (n=20). Three sections (A: motor cortex; B: cingulate and superior frontal gyri; C: caudate nucleus and putamen) with tau immunohistochemistry were scanned and converted to WSIs. We evaluated the models using 5-fold cross-validation. Attention-based interpretation analysis was performed to understand the morphological features contributing to the diagnosis. Within highly attended regions, we also augmented gradient-weighted class activation mapping (Grad-CAM) to the model to visualize cellular-level evidence of the model's decisions.

Results The model trained in Section B showed the highest area under the curve (0.970 ± 0.037) and diagnostic accuracy (0.873 ± 0.087). The heatmap showed the highest attention in the gray matter of the superior frontal gyrus in AD and the white matter of the cingulate gyrus in CBD. The Grad-CAM showed the highest attention in neurofibrillary tangles in AD, threads in the white matter in CBD, globular astrocytic lesions in GGT, Pick bodies in PiD and coiled bodies in PSP.

Conclusions Our diagnostic pipeline could diagnose five tauopathies with high accuracy (approximately 87%). These findings demonstrated the feasibility of CLAM for the classification task on WSIs, which encourages further investigation, focusing on clinicopathological correlation studies.

Authors/Disclosures
Shunsuke Koga, MD, PhD (Hospital of the University of Pennsylvania) PRESENTER	Dr. Koga has nothing to disclose.
	No disclosure on file
Hiroaki Sekiya, MD, PhD (Mayo Clinic)	Dr. Sekiya has nothing to disclose.
	No disclosure on file
	No disclosure on file
	No disclosure on file
Dennis W. Dickson, MD (Mayo Clinic)	Dr. Dickson has nothing to disclose.
	No disclosure on file