Evidence map›Paper›PMID 36732684›Full record

ArticleBMC medical imaging2023

Automated assessment of the smoothness of retinal layers in optical coherence tomography images using a machine learning algorithm.

Jamshid Saeidian, Tahereh Mahmoudi, Hamid Riazi-Esfahani, Zahra Montazeriani, Alireza Khodabande, Mohammad Zarei, Nazanin Ebrahimiadib, Behzad Jafari, Alireza Afzal Aghaei, Hossein Azimi and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 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
–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. 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

11 authors.

Jamshid SaeidianFaculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Ave, Tehran, Iran.
Tahereh MahmoudiDepartment of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences and Research Center for Science and Technology in Medicine, Tehran, Iran.
Hamid Riazi-EsfahaniRetina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Zahra MontazerianiDepartment of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences and Research Center for Science and Technology in Medicine, Tehran, Iran.
Alireza KhodabandeRetina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad ZareiRetina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Nazanin EbrahimiadibRetina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Behzad JafariRetina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Alireza Afzal AghaeiDepartment of Computer Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
Hossein AzimiFaculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Ave, Tehran, Iran. hosseinazimi0213120@gmail.com.
Elias Khalili PourRetina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantifying the smoothness of different layers of the retina can potentially be an important and practical biomarker in various pathologic conditions like diabetic retinopathy. The purpose of this study is to develop an automated machine learning algorithm which uses support vector regression method with wavelet kernel and automatically segments two hyperreflective retinal layers (inner plexiform layer (IPL) and outer plexiform layer (OPL)) in 50 optical coherence tomography (OCT) slabs and calculates the smoothness index (SI). The Bland-Altman plots, mean absolute error, root mean square error and signed error calculations revealed a modest discrepancy between the manual approach, used as the ground truth, and the corresponding automated segmentation of IPL/ OPL, as well as SI measurements in OCT slabs. It was concluded that the constructed algorithm may be employed as a reliable, rapid and convenient approach for segmenting IPL/OPL and calculating SI in the appropriate layers.

Indexed as

RetinaTomography, Optical CoherenceAlgorithmsHumansAutomated segmentationBiomarkerBland-Altman plotInner plexiform layerOuter plexiform LayerSupport vector regression

Identifiers

PMID36732684
PMCPMC9896782

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.