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Applications of Artificial Intelligence and Machine Learning in Spine MRI
Aric Lee1, Wilson Ong1, Andrew Makmur1,2
1Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore.
Bioengineering (Basel, Switzerland)
|September 27, 2024
Summary
Artificial intelligence (AI) and machine learning (ML) are enhancing spine MRI diagnostics, improving image acquisition, analysis, and patient care. Future research should focus on foundation models and real-world clinical implementation.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Medicine
- Machine Learning Applications
Background:
- Diagnostic imaging, especially Magnetic Resonance Imaging (MRI), is crucial for assessing spine pathologies.
- Advancements in artificial intelligence (AI) and machine learning (ML) offer new applications in spine MRI.
Purpose of the Study:
- To review and examine the current applications of AI and ML in spine MRI.
- To identify key research themes, gaps, and future directions in this field.
Main Methods:
- A systematic literature search was performed across major databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) following PRISMA guidelines.
- 50 studies were selected from 1226 initial results for data extraction and thematic categorization.
- Studies were categorized into Image Acquisition and Processing, Segmentation, Diagnosis and Treatment Planning, and Patient Selection and Prognostication.
Main Results:
- AI demonstrates significant potential to improve various aspects of spine MRI, including image acquisition, processing, segmentation, and diagnostic capabilities.
- Current research highlights AI's role in diagnosis, treatment planning, patient selection, and prognostication.
- Thematic analysis revealed key areas of AI application and development within spine MRI.
Conclusions:
- AI and ML are poised to revolutionize spine MRI by enhancing efficiency and accuracy across the clinical workflow.
- Future research should prioritize foundation models, large-language models, and real-world clinical validation to address generalizability and implementation challenges.
- Collaborative efforts are essential to maximize the benefits of AI in spine MRI for improved patient outcomes.
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