Role of Machine Learning in Management of Degenerative Spondylolisthesis

A Systematic Review

Sherif El-Daw, MD; Ahmad El-Tantawy, MD; Tarek Aly, MD; Mohamed Ramadan, MD

Disclosures

Curr Orthop Pract. 2021;32(3):302-308. 

In This Article

Abstract and Introduction

Abstract

Background: Machine learning is a field of artificial intelligence that allows a computer system to learn through repetitive processes and improve with experience. Precise study of medical data benefits early disease recognition, patient care, and community services.

Methods: The purpose of this systematic review was to assess the evidence for effectiveness of machine learning and artificial intelligence in the management of spondylolisthesis. A literature search of published and unpublished articles resulted in the retrieval of more than 1000 potential studies on the subject area. Eight were reviewed according to inclusion criteria.

Results: Expert medical doctors examined the pelvis and lumbar spine shape and orientation to diagnose spondylolisthesis. However, some shape and orientation parameters were misleading and unclear. Therefore, automatic diagnosis methods (classification methods) have been proposed to help medical doctors. The most important parameter of classification was found to be the grade of spondylolisthesis.

Conclusions: Although the proposed results may be misleading, the studies provided evidence to suggest that two-thirds of the patients with grade I spondylolisthesis were stable enough to tolerate decompression without fusion, but that one-third of the patients appeared to develop instability over time. This instability often led to reoperation for spinal fusion at the level of listhesis. It is possible to create a predictive machine learning algorithm that is calibrated and accurate to predict discharge placement.

Level of Evidence: Level I.

Introduction

Machine learning is a process that develops algorithms to organize data, gather information, and make expectations based on the analysis of the data. Depending upon the artificial intelligence of the computer programs, the program improves and upgrades itself with additional data. Then, it can predict newer data.[1]

The benefit of gathering different types of data is the recognition of problems as early as possible to improve patient care. Algorithms of machine learning in the practice of medicine were developed to use medical data that include patient files, imaging, and laboratory tests.[2] Machine learning algorithms are based on the amounts and types of data.[3] These algorithms can evaluate and assess large amounts of data, which is a great advantage over the average human mind.[4,5] Neurological research scientists used the cell structure of the brain to build a computer algorithm known as an artificial neural network (ANN) that has been used for search and assessment of patients' data to elucidate useful results.[6]

Application of machine learning in the medical field helps clinicians predict outcomes from the early phases of disease.[7] Using machine learning for the evaluation and management of diseases other than spinal conditions (eg. diabetes, cardiac problems, and breast cancer) has been studied by many authors.[8–17] Spondylolisthesis, which is the displacement of one vertebra over another, can occur in children or adults and have congenital or acquired causes. There has been no consensus on the best management of this condition; the merits of the surgical techniques of reduction, fusion, or instrumentation are still disputed.[18–20]

This review aimed to assess current literature regarding the importance of machine learning in the management of degenerative spondylolisthesis and focus on the main points of diagnosis and treatment of this condition. The authors also aimed to discover if artificial intelligence and machine learning have any roles in improving the management of degenerative spondylolisthesis.

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