Title: Opportunities and challenges of machine learning models for prediction and diagnosis of spondylolisthesis: a systematic review

Authors: Deepika Saravagi; Shweta Agrawal; Manisha Saravagi

Addresses: Department of Computer Application, SAGE University, Indore, M.P., 452012, India ' CSE, SIRT, SAGE University, Indore, M.P., 452012, India ' Physiotherapy Department, Railway Hospital, Kota, Rajasthan, 324002, India

Abstract: Applications of machine learning algorithms in healthcare domain gained immense popularity and attracted research communities in the last decade. Interdisciplinary collaboration leads to development of new models to investigate issues related to the spondylolisthesis (slippage of one vertebrae over another) with promising results and large potential. This paper summarises available machine learning models to detect and predict spondylolisthesis. It would be a valuable resource from modelling and application perspective. We extracted papers by systematic searching of databases: Scopus, PubMed, IEEE, Google Scholar, ResearchGate, Springer and Elsevier with preset inclusion-exclusion criteria. Articles were analysed as per title, abstract, and full-text review. Finally, opportunities and challenges in this area is discussed. We examined the specific models and frameworks employed, and the overall performance achieved according to the metrics used at each work under study. Our findings indicate that machine learning model can provide high accuracy and outperforms in existing image processing techniques.

Keywords: diagnosis; grading; machine learning; spondylolisthesis.

DOI: 10.1504/IJESMS.2021.115534

International Journal of Engineering Systems Modelling and Simulation, 2021 Vol.12 No.2/3, pp.127 - 138

Received: 23 Sep 2020
Accepted: 16 Oct 2020
Published online: 07 Jun 2021 *

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