Deep learning shows no morphological abnormalities in neutrophils in Alzheimer's disease

Chabrun, Floris; Dieu, Xavier; Doudeau, Nicolas; Gautier, Jennifer; Luque‐Paz, Damien; Geneviève, Franck; Ferré, Marc; Mirebeau‐Prunier, Delphine; Annweiler, Cédric; Reynier, Pascal

INTRODUCTION: Several studies have provided evidence of the key role of neutrophils in the pathophysiology of Alzheimer's disease (AD). Yet, no study to date has investigated the potential link between AD and morphologically abnormal neutrophils on blood smears. METHODS: Due to the complexity and subjectivity of the task by human analysis, deep learning models were trained to predict AD from neutrophil images. Control models were trained for a known feasible task (leukocyte subtype classification) and for detecting potential biases of overfitting (patient prediction). RESULTS: Deep learning models achieved state‐of‐the‐art results for leukocyte subtype classification but could not accurately predict AD. DISCUSSION: We found no evidence of morphological abnormalities of neutrophils in AD. Our results show that a solid deep learning pipeline with positive and bias control models with visualization techniques are helpful to support deep learning model results.