Evidence map›Paper›PMID 36207566›Full record

ArticleInternational ophthalmology2023

Screening of idiopathic epiretinal membrane using fundus images combined with blood oxygen saturation and vascular morphological features.

Kun Chen, Jianbo Mao, Hui Liu, Xiaona Wang, Peng Dou, Yu Lu, Mingzhai Sun, Lijun Shen, Lei Liu

Open access · bronzeAbstract read
PubMed Full text at PMC 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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
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, 7 citations in OpenAlex.

  1. Review
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

9 authors at 4 institutions in 1 country.

Kun ChenSchool of Biomedical Engineering & Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, Jiangsu, China.
Jianbo MaoEye Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Hui LiuSchool of Biomedical Engineering & Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, Jiangsu, China.
Xiaona WangThe First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Peng DouSchool of Biomedical Engineering & Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, Jiangsu, China.
Yu LuHefei Orbis Biotech, LTD, Hefei, Anhui, China.
Mingzhai SunSchool of Biomedical Engineering & Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, Jiangsu, China.
Lijun ShenEye Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China. slj@mail.eye.ac.cn.
Lei LiuSchool of Biomedical Engineering & Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, Jiangsu, China. liulei13@ustc.edu.cn.
Suzhou University of Science and Technology · CNAffiliated Eye Hospital of Wenzhou Medical College · CNFirst Affiliated Hospital of Harbin Medical University · CNUniversity of Science and Technology of China · CN

Funding

Key Research and Development Program of Anhui Province under Grant 202004h07020015Natural Science Foundation of Anhui Province under Grant 2008085MH259
6 · The paper itself

Abstract

purposeTo achieve an accurate diagnosis of idiopathic epiretinal membrane (iERM) through analyzing retinal blood vessel oxygen saturation (SO2) and vascular morphological features in fundus images.

methodsDual-modal fundus camera was used to obtain color fundus image, 570-nm, and 610-nm images. As iERM affects the macular area, a macular-centered semicircle area as region of interest (MROI) was selected and analyzed SO2 and vascular morphologies in it. Eventually, random forest (RF) and support vector machine (SVM) were as classifiers to diagnose iERM patients.

resultsThe arterial and venous SO2 levels of the iERM group were significantly higher than that of the control group. There were significant differences in the vessel density and fractal dimension on the artery for vascular morphology, while the tortuosity had a significant difference in the vein. By feeding the SO2 and the vascular morphological features into classifiers, an accuracy of over 82% was obtained, which is significantly better than the two separate inputs for classification.

conclusionSignificant differences in SO2 and vascular morphologies between control and iERM groups confirmed that the occurrence of iERM may affect blood supply and vascular structures. Benefiting from the dual-modal fundus camera and machine learning models, accurate judgments can be made on fundus images. Extensive experiments proved the importance of blood vessel SO2 and vascular morphologies for diagnosis, which is of great significance for clinical screening.

Indexed as

Epiretinal MembraneFluorescein AngiographyFundus OculiHumansOxygenOxygen SaturationRetinal VesselsOxygenDiagnosisFundus imagesIdiopathic epiretinal membrane (iERM)Machine learningOxygen saturation levelVascular morphology

Identifiers

PMID36207566
PMCPMC9543935
OpenAlexW4303454027

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.