Evidence map›Paper›PMID 38790333›Full record

ArticleBioengineering (Basel, Switzerland)2024

Morphology and Composition of Lumbar Intervertebral Discs: Comparative Analyses of Manual Measurement and Computer-Assisted Algorithms.

Yiting Cheng, Yuyan Ma, Kang Li, Celal Gungor, Richard Sesek, Ruoliang Tang

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2024. 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. Article
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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

6 authors.

Yiting ChengSchool of Mechanical Engineering, Sichuan University, Chengdu 610000, China.
Yuyan MaSichuan University-Pittsburgh Institute (SCUPI), Sichuan University, Chengdu 610000, China.
Kang LiWest China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610000, China.
Celal GungorDepartment of Forest Industrial Engineering, Izmir Katip Celebi University, Cigli 35620, Turkey.ORCID 0000-0001-9882-6393
Richard SesekDepartment of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA.
Ruoliang TangSichuan University-Pittsburgh Institute (SCUPI), Sichuan University, Chengdu 610000, China.ORCID 0000-0002-5960-7451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe morphology and internal composition, particularly the nucleus-to-cross sectional area (NP-to-CSA) ratio of the lumbar intervertebral discs (IVDs), is important information for finite element models (FEMs) of spinal loadings and biomechanical behaviors, and, yet, this has not been well investigated and reported.

methodsAnonymized MRI scans were retrieved from a previously established database, including a total of 400 lumbar IVDs from 123 subjects (58 F and 65 M). Measurements were conducted manually by a spine surgeon and using two computer-assisted segmentation algorithms, i.e., fuzzy C-means (FCM) and region growing (RG). The respective results were compared. The influence of gender and spinal level was also investigated.

resultsRatios derived from manual measurements and the two computer-assisted algorithms (FCM and RG) were 46%, 39%, and 38%, respectively. Ratios derived manually were significantly larger.

conclusionsComputer-assisted methods provide reliable outcomes that are traditionally difficult for the manual measurement of internal composition. FEMs should consider the variability of NP-to-CSA ratios when studying the biomechanical behavior of the spine.

Indexed as

computer-assisted segmentationcross-sectional areafinite element modelingfuzzy C-meansintervertebral disclumbar spinenucleus pulposusregion growing

Identifiers

PMID38790333
PMCPMC11117579

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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.