Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or a...

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Main Authors: Saravi, Babak Ebrahimzadeh (Author) , Hassel, Frank (Author) , Ülkümen, Sara (Author) , Zink, Alisia (Author) , Shavlokhova, Veronika (Author) , Couillard-Després, Sébastien (Author) , Boeker, Martin (Author) , Obid, Peter (Author) , Lang, Gernot Michael (Author)
Format: Article (Journal)
Language:English
Published: 22 March 2022
In: Journal of Personalized Medicine
Year: 2022, Volume: 12, Issue: 4, Pages: 1-24
ISSN:2075-4426
DOI:10.3390/jpm12040509
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3390/jpm12040509
Verlag, lizenzpflichtig, Volltext: https://www.mdpi.com/2075-4426/12/4/509
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Author Notes:Babak Saravi, Frank Hassel, Sara Ülkümen, Alisia Zink, Veronika Shavlokhova, Sebastien Couillard-Despres, Martin Boeker, Peter Obid and Gernot Michael Lang

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520 |a Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present 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. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. 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. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time. 
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