Applications of Artificial Intelligence and Machine Learning in Spine MRI

• Published in: Bioengineering (Basel) Vol. 11; no. 9; p. 894
• Main Authors: Lee, Aric, Ong, Wilson, Makmur, Andrew, Ting, Yong Han, Tan, Wei Chuan, Lim, Shi Wei Desmond, Low, Xi Zhen, Tan, Jonathan Jiong Hao, Kumar, Naresh, Hallinan, James T P D
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.09.2024; MDPI
• Subjects: Algorithms; Artificial intelligence; Bone marrow; Cost control; Deep learning; Fractures; Image acquisition; Image processing; Image segmentation; Infections; Learning algorithms; Literature reviews; Machine learning; Magnetic resonance imaging; Medical imaging; Pathology; Patients; Review; Spinal cord; spine; Technology assessment; Trauma; Tumors; magnetic resonance imaging; spine; spinal cord; machine learning; artificial intelligence
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11090894

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.