Evidence map›Paper›PMID 38383922›Full record

ArticleEuropean radiology2024

Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI.

Jasper W van der Graaf, Miranda L van Hooff, Bram van Ginneken, Merel Huisman, Matthieu Rutten, Dominique Lamers, Nikolas Lessmann, Marinus de Kleuver

Open access · hybridAbstract readValidation Study
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
4.0field-weighted citation impact, top 7% 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

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
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  6. The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025
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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 1 institution in 1 country.

Jasper W van der GraafDiagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands. jasper.vandergraaf@gmail.com.ORCID http://orcid.org/0000-0001-8684-5175
Miranda L van HooffDepartment of Orthopedics, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
Bram van GinnekenDiagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands.
Merel HuismanDepartment of Medical Imaging, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
Matthieu RuttenDiagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands.
Dominique LamersDepartment of Orthopedics, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
Nikolas LessmannDiagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands.
Marinus de KleuverDepartment of Orthopedics, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
Radboud University Nijmegen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesSeverity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients. MATERIALS AND

methodsVertebrae and intervertebral discs were automatically segmented using a 3D AI algorithm, trained on 447 lumbar MRI series. The segmentations were used to calculate all possible angles between the vertebral endplates, with the largest being the Cobb angle. The results were validated with 50 high-resolution sagittal lumbar MRI scans of DS patients, in which three experienced readers measured the Cobb angle. Reliability was determined using the intraclass correlation coefficient (ICC).

resultsThe ICCs between the readers ranged from 0.90 (95% CI 0.83-0.94) to 0.93 (95% CI 0.88-0.96). The ICC between the maximum angle found by the algorithm and the average manually measured Cobb angles was 0.83 (95% CI 0.71-0.90). In 9 out of the 50 cases (18%), all readers agreed on both vertebral levels for Cobb angle measurement. When using the algorithm to extract the angles at the vertebral levels chosen by the readers, the ICCs ranged from 0.92 (95% CI 0.87-0.96) to 0.97 (95% CI 0.94-0.98).

conclusionThe Cobb angle can be accurately measured on MRI using the newly developed algorithm in patients with DS. The readers failed to consistently choose the same vertebral level for Cobb angle measurement, whereas the automatic approach ensures the maximum angle is consistently measured. CLINICAL RELEVANCE STATEMENT: Our AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients, potentially reducing the reliance on conventional radiographs, ensuring consistent assessments, and therefore improving patient care. KEY POINTS: • While often available, MRI images are rarely utilized to determine the severity of degenerative scoliosis. • The presented MRI Cobb angle algorithm is more reliable than humans in patients with degenerative scoliosis. • Radiographic imaging for Cobb angle measurements is mitigated when lumbar MRI images are available.

Indexed as

AlgorithmsLumbar VertebraeMagnetic Resonance ImagingScoliosisAdultAgedAged, 80 and overArtificial IntelligenceFemaleHumansImaging, Three-DimensionalMaleMiddle AgedReproducibility of ResultsRetrospective StudiesCobb angleDeep learningMagnetic resonance imagingScoliosisSpine

Identifiers

PMID38383922
PMCPMC11364572
OpenAlexW4392018632

What OpenQuestion holds

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LicenceCC BY
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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.