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

• Verfasst von: Saravi, Babak Ebrahimzadeh [VerfasserIn]
• Verfasst von: Hassel, Frank [VerfasserIn]
• Verfasst von: Ülkümen, Sara [VerfasserIn]
• Verfasst von: Zink, Alisia [VerfasserIn]
• Verfasst von: Shavlokhova, Veronika [VerfasserIn]
• Verfasst von: Couillard-Després, Sébastien [VerfasserIn]
• Verfasst von: Boeker, Martin [VerfasserIn]
• Verfasst von: Obid, Peter [VerfasserIn]
• Verfasst von: Lang, Gernot Michael [VerfasserIn]
• Titel: Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models
• Verf.angabe: Babak Saravi, Frank Hassel, Sara Ülkümen, Alisia Zink, Veronika Shavlokhova, Sebastien Couillard-Despres, Martin Boeker, Peter Obid and Gernot Michael Lang
• E-Jahr: 2022
• Jahr: 22 March 2022
• Umfang: 24 S.
• Fussnoten: This article belongs to the Special Issue Application of Artificial Intelligence in Personalized Medicine ; Gesehen am 02.08.2022
• Titel Quelle: Enthalten in: Journal of Personalized Medicine
• Ort Quelle: Basel : MDPI, 2011
• Jahr Quelle: 2022
• Band/Heft Quelle: 12(2022), 4, special issue vom: Apr., Artikel-ID 509, Seite 1-24
• ISSN Quelle: 2075-4426
• DOI: doi:10.3390/jpm12040509
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: artificial intelligence
• Sach-SW: deep learning
• Sach-SW: deep neural networks
• Sach-SW: degeneration
• Sach-SW: healthcare
• Sach-SW: hybrid networks
• Sach-SW: machine learning
• Sach-SW: mixed data
• Sach-SW: multi-input
• Sach-SW: prediction
• Sach-SW: spine
• K10plus-PPN: 1812798415

Abstract

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.