Evidence map›Paper›PMID 40567778›Full record

ArticlePeerJ. Computer science2025

Classification of the stages of Alzheimer's disease based on three-dimensional lightweight neural networks.

Jun Li, Juntong Liu, Yang Su, Jie Chang, Mingquan Ye

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jun LiSchool of Medical Information, Wannan Medical College, Wuhu, China.ORCID 0000-0002-2478-2997
Juntong LiuSchool of Medical Information, Wannan Medical College, Wuhu, China.
Yang SuSchool of Medical Information, Wannan Medical College, Wuhu, China.
Jie ChangSchool of Medical Information, Wannan Medical College, Wuhu, China.
Mingquan YeSchool of Medical Information, Wannan Medical College, Wuhu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease is a neurodegenerative disease that seriously threatens the life and health of the elderly. This study used three-dimensional lightweight neural networks to classify the stages of Alzheimer's disease and explore the relationship between the stages and the variations of brain tissue. The study used CAT12 to preprocess magnetic resonance images of the brain and got three kinds of preprocessed images: standardized images, segmented standardized gray matter images, and segmented standardized white matter images. The three kinds of images were used to train four kinds of three-dimensional lightweight neural networks respectively, and the evaluation metrics of the neural networks are calculated. The accuracies of the neural networks for classifying the stages of Alzheimer's disease (cognitively normal, mild cognitive impairment, Alzheimer's disease) in the study are above 96%, and the precisions and recalls of classifying the three stages are above 94%. The study found that for the classification of cognitively normal, the best classification results can be obtained by training with the segmented standardized gray matter images, and for mild cognitive impairment and Alzheimer's disease, the best classification results can be obtained by training with the standardized images. The study analyzed that in the process of cognitively normal to mild cognitive impairment, variations in the segmented standardized gray matter images are more obvious at the beginning, while variations in the segmented standardized white matter images are not obvious. As the disease progresses, variations in the segmented standardized white matter images tend to become more significant, and variations in the segmented standardized gray matter images and white matter images are both significant in the development of Alzheimer's disease.

Indexed as

Alzheimer’s diseaseClassificationMagnetic resonance imagingThree-dimensional lightweight neural networks

Identifiers

PMID40567778
PMCPMC12192899

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.