Commentary: Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning

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Published inNeurosurgery Vol. 89; no. 1; pp. E16 - E17
Main Author Fourney, Daryl R
Format Journal Article
LanguageEnglish
Published United States Oxford University Press 01.07.2021
Wolters Kluwer Health, Inc
Subjects
Algorithms
Back pain
Back surgery
Clinical medicine
Health care policy
Hospitals
Machine learning
Magnetic resonance imaging
Medical referrals
Neurosurgery
Patient satisfaction
Systematic review
Online AccessGet full text

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Author Fourney, Daryl R
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Cites_doi 10.1097/BRS.0b013e3181c7cf4b
10.3171/2013.10.SPINE13434
10.1097/BRS.0000000000000574
10.1017/S0317167100051362
10.1007/s00586-018-5673-2
10.1016/j.spinee.2007.10.005
10.1007/s00586-020-06675-2
10.1503/cjs.003713
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Copyright Congress of Neurological Surgeons 2021. 2021
Congress of Neurological Surgeons 2021.
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References 33826737 - Neurosurgery. 2021 Apr 07
Wilson (bib5-20231011)
Oliveira (bib1-20231011) 2018; 27
Kindrachuk (bib9-20231011) 2014; 20
Dagenais (bib2-20231011) 2008; 8
Fourney (bib4-20231011) 2010; 37
Coeckelberghs (bib8-20231011) 2021
Busse (bib6-20231011) 2014; 57
Barz (bib7-20231011) 2010; 35
Wilgenbusch (bib10-20231011) 2014; 39
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SubjectTerms Algorithms
Back pain
Back surgery
Clinical medicine
Health care policy
Hospitals
Machine learning
Magnetic resonance imaging
Medical referrals
Neurosurgery
Patient satisfaction
Systematic review
Title Commentary: Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning
URI https://www.ncbi.nlm.nih.gov/pubmed/33989412
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