Evidence map›Paper›PMID 33826737›Full record

ArticleNeurosurgery2021

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

Bayard Wilson, Bilwaj Gaonkar, Bryan Yoo, Banafsheh Salehi, Mark Attiah, Diane Villaroman, Christine Ahn, Matthew Edwards, Azim Laiwalla, Anshul Ratnaparkhi and 4 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Neurosurgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 pooled it
3.2field-weighted citation impact, top 8% 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

23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Developing a triage predictive model for access to a spinal surgeon using clinical variables and natural language processing of radiology reports.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 · 2026
    Article
  6. Article
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  8. Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database.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
    Article
  9. Article
  10. Review
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  12. Article
  13. The Application of Artificial Intelligence in Spine Surgery: A Scoping Review.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2025
    Article
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  18. Experience in psychological counseling supported by artificial intelligence technology.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024
    Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 1 institution in 1 country.

Bayard WilsonDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.ORCID 0000-0001-6782-0677
Bilwaj GaonkarDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Bryan YooDepartment of Radiology, University of California, Los Angeles, Los Angeles, California, USA.
Banafsheh SalehiDepartment of Radiology, University of California, Los Angeles, Los Angeles, California, USA.
Mark AttiahDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Diane VillaromanDepartment of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California, USA.
Christine AhnDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Matthew EdwardsDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Azim LaiwallaDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Anshul RatnaparkhiDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Ien LiDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Kirstin CookDavid Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
Joel BeckettDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Luke MacyszynDepartment of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
University of California, Los Angeles · US

Funding

Developing Database and Software infrastructure for Quantitative Radiologic Analysis of Lumbar RadiculopathyR21EB026665 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MACYSZYN, LUKE · 2019 to 2020
$429k
NIBIB NIH HHS R21 EB026665
6 · The paper itself

Abstract

backgroundThe referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative.

objectiveTo develop a machine-learning-based algorithm which accurately identifies patients as candidates for consultation with a spine surgeon, using only magnetic resonance imaging (MRI).

methodsWe trained a deep U-Net machine learning model to delineate spinal canals on axial slices of 100 normal lumbar MRI scans which were previously delineated by expert radiologists and neurosurgeons. We then tested the model against lumbar MRI scans for 140 patients who had undergone lumbar spine MRI at our institution (60 of whom ultimately underwent surgery, and 80 of whom did not). The model generated automated segmentations of the lumbar spinal canals and calculated a maximum degree of spinal stenosis for each patient, which served as our biomarker for surgical pathology warranting expert consultation.

resultsThe machine learning model correctly predicted surgical candidacy (ie, whether patients ultimately underwent lumbar spinal decompression) with high accuracy (area under the curve = 0.88), using only imaging data from lumbar MRI scans.

conclusionAutomated interpretation of lumbar MRI scans was sufficient to correctly determine surgical candidacy in nearly 90% of cases. Given that a significant proportion of referrals placed for spine surgery evaluation fail to meet criteria for surgical intervention, our model could serve as a valuable tool for patient triage and thereby address some of the inefficiencies within the outpatient surgical referral process.

Indexed as

Machine LearningSpinal StenosisDecompression, SurgicalFemaleHumansLumbar VertebraeMagnetic Resonance ImagingMaleMiddle AgedMachine learningMRISpine surgeryTriage

Identifiers

PMID33826737
PMCPMC8203423
OpenAlexW3147224310

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.