Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

Healthcare systems generate large amounts of data. Determining patterns and variations in genomic, radiological, laboratory, or clinical data allows high predictive accuracy in health-related tasks. Convolutional neural networks are applied to image data. Use for non-imaging data becomes feasible through machine learning techniques, converting non-imaging data into images. Healthcare providers use a combination of patient information to train a hybrid deep learning model. This approach simulates natural human behavior.

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  The review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery.
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  This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery.
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  The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data.