ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2021
Deep learning shows no morphological abnormalities in neutrophils in Alzheimer's disease.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Automated Segmentation and Morphometric Analysis of Thioflavin-S-Stained Amyloid Deposits in Alzheimer's Disease Brains and Age-Matched Controls Using Weakly Supervised Deep Learning.International journal of molecular sciences · 2025Article
- Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.Sensors (Basel, Switzerland) · 2023Review
- Deep learning shows no morphological abnormalities in neutrophils in Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSeveral 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.
methodsDue 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).
resultsDeep 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.
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