Evidence map›Paper›PMID 36083563›Full record

ArticleInternational ophthalmology2023

Approach to glaucoma diagnosis and prediction based on multiparameter neural network.

Qi Li, Ningli Wang, Zhicheng Liu, Lin Li, Zhicheng Liu, Xiaoxue Long, Hongyu Yang, Hongfang Song

Abstract read
PubMed Publisher
In one paragraph

Article in International ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

8 authors.

Qi LiSchool of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Ningli WangBeijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
Zhicheng LiuSchool of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Lin LiSchool of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Zhicheng LiuSchool of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Xiaoxue LongSchool of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Hongyu YangSchool of Biomedical Engineering, Capital Medical University, Beijing, 100069, China.
Hongfang SongSchool of Biomedical Engineering, Capital Medical University, Beijing, 100069, China. songhf@ccmu.edu.cn.ORCID http://orcid.org/0000-0001-9161-7994

Funding

beijing natural science foundation 7152022national natural science foundation of china 10802503
6 · The paper itself

Abstract

purposeTo investigate the effect of comprehensive factor analysis on the relationship between glaucoma assessment and combined parameters including trans-laminar cribrosa pressure difference (TLCPD) and fractional pressure reserve (FPR).

methodsThe clinical data of 1029 patients with 15 indicators from the medical records of Beijing Tongren Hospital and 600 cases with 1322 indicators from Beijing Eye Research were collected. The doc2vec method was used to vectorize. The multivariate imputation by chained equations (MICE) method was used to interpolate. The original data combined with TLCPD, combined with FPR, and not combined parameters were respectively applied to train the neural network based on VGG16 and autoencoder to predict glaucoma and to evaluate the effect of combined parameters.

resultsThe accuracy rates used to classify the glaucoma of the two sets reach over 0.90, and the precision rates reach 0.70 and 0.80 respectively. After using TLCPD and FPR for the autoencoder method, the accuracy rates are both close to 1.0, and the precision rates are 0.90 and 0.70 respectively.

conclusionUsing the combined parameters of FPR and TLCPD can effectively improve the diagnosis and prediction of glaucoma. Compared with TLCPD, FPR is more suitable for improving the effect of neural network for glaucoma classification.

Indexed as

GlaucomaIntraocular PressureHumansPressureComputer-aided diagnosisFractional pressure reserveGlaucomaNeural networkTrans-laminar cribrosa pressure difference

Identifiers

PMID36083563

What OpenQuestion holds

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Registered trials

None linked

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.