Advancing spine care through AI and machine learning: overview and applications

Area of Science:

• Spine care and research
• Artificial intelligence
• Machine learning

Background:

• Machine learning (ML) is vital for advancing spine care and research.
• It leverages extensive healthcare data for improved diagnostics, decision support, and treatment selection.
• ML's capabilities are particularly significant in analyzing radiological images for spine conditions.

Purpose of the Study:

• To explore the applications of machine learning in spine care and research.
• To detail various ML techniques and their relevance to spinal data analysis.
• To emphasize the importance of validation for ML models in clinical settings.

Main Methods:

• Discussion of supervised and unsupervised learning, regression, classification, and clustering.
• Overview of ML algorithms including linear models, support vector machines, decision trees, neural networks, and deep convolutional neural networks.
• Analysis of diverse data types: visual, tabular, omics, and multimodal.

Main Results:

• ML aids in radiological image analysis, including localization, labeling, and detection of findings.
• ML models can predict clinical outcomes, supporting personalized medicine approaches.
• Internal and external validation are crucial for assessing ML model performance and reliability.

Conclusions:

• Machine learning offers significant potential to revolutionize spine care through enhanced diagnostics and personalized treatment strategies.
• The application of various ML algorithms across diverse data types can lead to more accurate and effective spine care.
• Rigorous validation of ML models is essential for their successful implementation in clinical practice and research.