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. 2022 Apr;45(2):965-978.
doi: 10.1007/s10143-021-01624-z. Epub 2021 Sep 7.

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review

Mark E Stephens  1 , Christen M O'Neal  1 , Alison M Westrup  1 , Fauziyya Y Muhammad  1 , Daniel M McKenzie  1 , Andrew H Fagg  2 , Zachary A Smith  3
Affiliations

Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review

Mark E Stephens et al. Neurosurg Rev. 2022 Apr.

Abstract

Machine learning is a rapidly evolving field that offers physicians an innovative and comprehensive mechanism to examine various aspects of patient data. Cervical and lumbar degenerative spine disorders are commonly age-related disease processes that can utilize machine learning to improve patient outcomes with careful patient selection and intervention. The aim of this study is to examine the current applications of machine learning in cervical and lumbar degenerative spine disease. A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of PubMed, Embase, Medline, and Cochrane was conducted through May 31st, 2020, using the following terms: "artificial intelligence" OR "machine learning" AND "neurosurgery" AND "spine." Studies were included if original research on machine learning was utilized in patient care for degenerative spine disease, including radiographic machine learning applications. Studies focusing on robotic applications in neurosurgery, navigation, or stereotactic radiosurgery were excluded. The literature search identified 296 papers, with 35 articles meeting inclusion criteria. There were nine studies involving cervical degenerative spine disease and 26 studies on lumbar degenerative spine disease. The majority of studies for both cervical and lumbar spines utilized machine learning for the prediction of postoperative outcomes, with 5 (55.6%) and 15 (61.5%) studies, respectively. Machine learning applications focusing on degenerative lumbar spine greatly outnumber the current volume of cervical spine studies. The current research in lumbar spine also demonstrates more advanced clinical applications of radiographic, diagnostic, and predictive machine learning models.

Keywords: Artificial intelligence; Cervical spondylotic myelopathy; Degenerative spine disease; Machine learning; Predictive modeling; Systematic review.

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References

    1. Ames CP, Smith JS, Pellisé F, Kelly M, Gum JL, Alanay A, Acaroğlu E, Pérez-Grueso FJS, Kleinstück FS, Obeid I, Vila-Casademunt A, Shaffrey CI Jr, Burton DC, Lafage V, Schwab FJ, Shaffrey CI Sr, Bess S, Serra-Burriel M (2019) Development of predictive models for all individual questions of SRS-22R after adult spinal deformity surgery: a step toward individualized medicine. Eur Spine J 28:1998–2011. https://doi.org/10.1007/s00586-019-06079-x - DOI - PubMed
    1. Ames CP, Smith JS, Pellisé F, Kelly M, Alanay A, Acaroğlu E, Pérez-Grueso FJS, Kleinstück F, Obeid I, Vila-Casademunt A, Shaffrey CI Jr, Burton D, Lafage V, Schwab F, Shaffrey CI Sr, Bess S, Serra-Burriel M (2019) Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine (Phila Pa 1976) 44:915–926. https://doi.org/10.1097/brs.0000000000002974 - DOI
    1. Arvind V, Kim JS, Oermann EK, Kaji D, Cho SK (2018) Predicting surgical complications in adult patients undergoing anterior cervical discectomy and fusion using machine learning. Neurospine 15:329–337. https://doi.org/10.14245/ns.1836248.124 - DOI - PubMed - PMC
    1. Azimi P, Benzel EC, Shahzadi S, Azhari S, Mohammadi HR (2014) Use of artificial neural networks to predict surgical satisfaction in patients with lumbar spinal canal stenosis: clinical article. J Neurosurg Spine 20:300–305. https://doi.org/10.3171/2013.12.Spine13674 - DOI - PubMed
    1. Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ (2018) Current applications and future impact of machine learning in radiology. Radiology 288:318–328. https://doi.org/10.1148/radiol.2018171820 - DOI - PubMed

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