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Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.
Wilson, Bayard; Gaonkar, Bilwaj; Yoo, Bryan; Salehi, Banafsheh; Attiah, Mark; Villaroman, Diane; Ahn, Christine; Edwards, Matthew; Laiwalla, Azim; Ratnaparkhi, Anshul; Li, Ien; Cook, Kirstin; Beckett, Joel; Macyszyn, Luke.
Affiliation
  • Wilson B; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Gaonkar B; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Yoo B; Department of Radiology, University of California, Los Angeles, Los Angeles, California, USA.
  • Salehi B; Department of Radiology, University of California, Los Angeles, Los Angeles, California, USA.
  • Attiah M; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Villaroman D; Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California, USA.
  • Ahn C; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Edwards M; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Laiwalla A; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Ratnaparkhi A; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Li I; David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
  • Cook K; David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
  • Beckett J; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
  • Macyszyn L; Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA.
Neurosurgery ; 89(1): 116-121, 2021 06 15.
Article in En | MEDLINE | ID: mdl-33826737
ABSTRACT

BACKGROUND:

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

OBJECTIVE:

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

METHODS:

We 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.

RESULTS:

The 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.

CONCLUSION:

Automated 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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Spinal Stenosis / Machine Learning Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Middle aged Language: En Journal: Neurosurgery Year: 2021 Document type: Article Affiliation country: United States
Fulltext
Add to My VHL
Print
XML
PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Spinal Stenosis / Machine Learning Type of study: Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Middle aged Language: En Journal: Neurosurgery Year: 2021 Document type: Article Affiliation country: United States