Evidence map›Paper›PMID 35844883›Full record

ArticleFrontiers in public health2022

A Study on the Correlation Between Age-Related Macular Degeneration and Alzheimer's Disease Based on the Application of Artificial Neural Network.

Meng Zhang, Xuewu Gong, Wenhui Ma, Libo Wen, Yuejing Wang, Hongbo Yao

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Review
  2. Article
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

6 authors.

Meng ZhangHistology and Embryology Section, Qiqihar Medical University, Qiqihar, China.
Xuewu GongThe Second Affiliated Hospital of Qiqihar Medical University, Qiqihar, China.
Wenhui MaComputer Experimental Teaching Center, Qiqihar Medical University, Qiqihar, China.
Libo WenPhysiology Section, Qiqihar Medical University, Qiqihar, China.
Yuejing WangHistology and Embryology Section, Qiqihar Medical University, Qiqihar, China.
Hongbo YaoHistology and Embryology Section, Qiqihar Medical University, Qiqihar, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Age-related Macular Degeneration (AMD) is a kind of irreversible vision loss or disease caused by retinal pigment epithelial cells and neuroretinal degeneration, which has become the main cause of vision loss and blindness of the elderly over 65 years old in developed countries. The main clinical manifestations are cognitive decline, mental symptoms and behavioral disorders, and the gradual decline of daily living ability. In this paper, a feature extraction method of electroencephalogram (EEG) signal based on multi-spectral image fusion of multi-brain regions is proposed based on artificial neural network (ANN). In this method, the brain is divided into several different brain regions, and the EEG signals of different brain regions are transformed into several multispectral images by combining with the multispectral image transformation method. Using Alzheimer's disease (AD) classification algorithm, the depth residual network model pre-trained in ImageNet was transferred to sMRI data set for fine adjustment, instead of training a brand-new model from scratch. The results show that the proposed method solves the problem of few available medical image samples and shortens the training time of ANN model.

Indexed as

Alzheimer DiseaseMacular DegenerationAgedAlgorithmsBrainHumansNeural Networks, Computerage-related macular degenerationAlzheimer's diseaseartificial neural networkcorrelationrelevance

Identifiers

PMID35844883
PMCPMC9280183

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.