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

Author:

Stephens Mark E.,O’Neal Christen M.,Westrup Alison M.,Muhammad Fauziyya Y.,McKenzie Daniel M.,Fagg Andrew H.,Smith Zachary A.

Publisher

Springer Science and Business Media LLC

Subject

Neurology (clinical),General Medicine,Surgery

Reference44 articles.

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

2. 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

3. 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

4. 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

5. 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

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