Evidence map›Paper›PMID 33137112›Full record

ArticlePloS one2020

Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis.

Friska Natalia, Hira Meidia, Nunik Afriliana, Julio Christian Young, Reyhan Eddy Yunus, Mohammed Al-Jumaily, Ala Al-Kafri, Sud Sudirman

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
1.3field-weighted citation impact, top 21% 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

11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 27 citations in OpenAlex.

  1. Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025
    Pooled it
  2. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022
    Pooled it
  3. Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021
    Pooled it
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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 at 3 institutions in 2 countries.

Friska NataliaFaculty of Engineering and Informatics, Universitas Multimedia Nusantara, Serpong, Indonesia.
Hira MeidiaFaculty of Engineering and Informatics, Universitas Multimedia Nusantara, Serpong, Indonesia.
Nunik AfrilianaFaculty of Engineering and Informatics, Universitas Multimedia Nusantara, Serpong, Indonesia.
Julio Christian YoungFaculty of Engineering and Informatics, Universitas Multimedia Nusantara, Serpong, Indonesia.
Reyhan Eddy YunusDr. Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Mohammed Al-JumailyDr. Sulaiman Al-Habib Hospital, Dubai, UAE.
Ala Al-KafriSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.ORCID 0000-0002-6825-9110
Sud SudirmanSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.ORCID 0000-0003-4083-0810
Multimedia Nusantara University · IDLiverpool John Moores University · GBRumah Sakit Umum Pusat Nasional Dr. Cipto Mangunkusumo · ID

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient's lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient's lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm (p = 0.92) and 0.29 mm (p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm (p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.

Indexed as

FemaleHumansIntervertebral Disc DegenerationIntervertebral Disc DisplacementLow Back PainLumbosacral RegionMagnetic Resonance ImagingMaleSpinal CanalSpinal Stenosis

Identifiers

PMID33137112
PMCPMC7605707
OpenAlexW3096765115

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.