Evidence map›Paper›PMID 42061991›Full record

ArticleInternational journal of spine surgery2026

Deep Learning-Based Identification of Surgical Candidacy for Cervical Spinal Cord Decompression.

Anshul Ratnaparkhi, Bayard Wilson, David Zarrin, Abhinav Suri, Jacob Alderete, David Berin, Simon Moore, Bryan Yoo, Banafsheh Salehi, Kirstin Cook and 5 more

Abstract read
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In one paragraph

Article in International journal of spine surgery, 2026. 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

15 authors.

Anshul RatnaparkhiDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Bayard WilsonDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
David ZarrinDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Abhinav SuriDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Jacob AldereteDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
David BerinDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Simon MooreDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Bryan YooDepartment of Radiological Sciences, University of California, Los Angeles, CA, USA.
Banafsheh SalehiDepartment of Radiological Sciences, University of California, Los Angeles, CA, USA.
Kirstin CookDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
T J FlorenceDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Azim LaiwallaDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Bilwaj GaonkarDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Luke MacyszynDepartment of Neurosurgery, University of California, Los Angeles, CA, USA.
Joel BeckettDepartment of Neurosurgery, University of California, Los Angeles, CA, USA joel.beckett@cshs.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence has previously demonstrated the capability to interpret cervical spine imaging. The present study aims to identify whether deep learning can be harnessed to triage patients into operative and nonoperative groups based on imaging findings, an intricate process complicated by the high rates of background findings.

methodsA deep-learning algorithm was trained to segment the spinal canal and spinal cord on 100 axial cervical spine magnetic resonance images to generate a biomarker for cervical stenosis, defined as the minimum difference in cross-sectional area between these structures. After training, the model and its biomarker were tested against a cohort of 147 consecutive patients evaluated in the outpatient setting by a group of board-certified neurosurgeons at our institution for complaints related to their cervical spines.

resultsThe mean minimum difference in cross-sectional area between the spinal canal and spinal cord for our cohort was 35.90 ± 25.00 mm

conclusionThe present work describes a proof-of-concept model for triaging patients with cervical stenosis into surgical and nonsurgical cohorts based on imaging findings. In doing this, we aim to provide an additional supportive metric for referring providers to consult when considering whether to refer patients to spine surgery. CLINICAL RELEVANCE: Neck pain is a common presenting symptom in the primary care setting, yet determining which patients could benefit from a surgical consultation is often a nuanced task. Deep learning has the potential to assist with the radiographic component of the referral process by identifying markers suggestive of operative candidacy, providing an objective metric for referring providers to consider when deciding whether surgical consultation is warranted. This may help triage patients by expediting referral of operative patients to spine surgeons and redirecting nonoperative patients to physiatry, physical therapy, or other conservative management. LEVEL OF EVIDENCE: 3:

Indexed as

cervical spine MRIcervical stenosisdeep learningimaging-derived biomarkerspinal cord compressionsurgical candidacy

Identifiers

PMID42061991
PMCPMC13154031

What OpenQuestion holds

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Registered trials

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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.