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Bioengineering
Volume 11
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10.3390/bioengineering11090894
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Review

Applications of Artificial Intelligence and Machine Learning in Spine MRI

by
Aric Lee
1,*,†,
Wilson Ong
1,†,
Andrew Makmur
1,2,
Yong Han Ting
1,2,
Wei Chuan Tan
1,
Shi Wei Desmond Lim
1,
Xi Zhen Low
1,2,
Jonathan Jiong Hao Tan
3,
Naresh Kumar
3 and
James T. P. D. Hallinan
1,2
1
Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
2
Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
3
National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Bioengineering 2024, 11(9), 894; https://doi.org/10.3390/bioengineering11090894
Submission received: 27 July 2024 / Revised: 1 September 2024 / Accepted: 1 September 2024 / Published: 5 September 2024
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Spine Research)
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Abstract

Diagnostic imaging, particularly MRI, plays a key role in the evaluation of many spine pathologies. Recent progress in artificial intelligence and its subset, machine learning, has led to many applications within spine MRI, which we sought to examine in this review. A literature search of the major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search yielded 1226 results, of which 50 studies were selected for inclusion. Key data from these studies were extracted. Studies were categorized thematically into the following: Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication. Gaps in the literature and the proposed areas of future research are discussed. Current research demonstrates the ability of artificial intelligence to improve various aspects of this field, from image acquisition to analysis and clinical care. We also acknowledge the limitations of current technology. Future work will require collaborative efforts in order to fully exploit new technologies while addressing the practical challenges of generalizability and implementation. In particular, the use of foundation models and large-language models in spine MRI is a promising area, warranting further research. Studies assessing model performance in real-world clinical settings will also help uncover unintended consequences and maximize the benefits for patient care.
Keywords: artificial intelligence; machine learning; spine; spinal cord; magnetic resonance imaging artificial intelligence; machine learning; spine; spinal cord; magnetic resonance imaging
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MDPI and ACS Style

Lee, A.; Ong, W.; Makmur, A.; Ting, Y.H.; Tan, W.C.; Lim, S.W.D.; Low, X.Z.; Tan, J.J.H.; Kumar, N.; Hallinan, J.T.P.D. Applications of Artificial Intelligence and Machine Learning in Spine MRI. Bioengineering 2024, 11, 894. https://doi.org/10.3390/bioengineering11090894

AMA Style

Lee A, Ong W, Makmur A, Ting YH, Tan WC, Lim SWD, Low XZ, Tan JJH, Kumar N, Hallinan JTPD. Applications of Artificial Intelligence and Machine Learning in Spine MRI. Bioengineering. 2024; 11(9):894. https://doi.org/10.3390/bioengineering11090894

Chicago/Turabian Style

Lee, Aric, Wilson Ong, Andrew Makmur, Yong Han Ting, Wei Chuan Tan, Shi Wei Desmond Lim, Xi Zhen Low, Jonathan Jiong Hao Tan, Naresh Kumar, and James T. P. D. Hallinan. 2024. "Applications of Artificial Intelligence and Machine Learning in Spine MRI" Bioengineering 11, no. 9: 894. https://doi.org/10.3390/bioengineering11090894

APA Style

Lee, A., Ong, W., Makmur, A., Ting, Y. H., Tan, W. C., Lim, S. W. D., Low, X. Z., Tan, J. J. H., Kumar, N., & Hallinan, J. T. P. D. (2024). Applications of Artificial Intelligence and Machine Learning in Spine MRI. Bioengineering, 11(9), 894. https://doi.org/10.3390/bioengineering11090894

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