BACKGROUND CONTEXT: Balloon kyphoplasty (BKP) is a commonly performed vertebral augmentation procedure for painful osteoporotic vertebral compression fractures (OVCFs). OBJECTIVE: This study aimed to support a non-inferiority finding for the use of a titanium implantable vertebral augmentation device (TIVAD) compared to BKP. STUDY DESIGN: Prospective, parallel group, controlled comparative randomized study.FDA device/drug status: The devices are FDA approved or approved by corresponding national agencies for this indication. The SpineJack Ò is commercially available in Europe. KyphX Xpander Ò Inflatable Bone Tamp is commercially available in the United States and Europe.

This prospective, consecutive, multicentre observational registry aimed to confirm the safety and clinical performance of the SpineJack system for the treatment of vertebral compression fractures (VCF) of traumatic origin. We enrolled 103 patients (median age: 61.6 years) with 108 VCF due to trauma, or traumatic VCF with associated osteoporosis. Primary outcome was back pain intensity (VAS). Secondary outcomes were Oswestry Disability Index (ODI), EuroQol-VAS, and analgesic consumption. 48 hours after surgery, a median relative decrease in pain intensity of 81.5% was observed associated with a significant reduction in analgesic intake. Improvements in disability (91.3% decrease in ODI score) and in quality of life (increase 21.1% of EQ-VAS score) were obtained 3 months after surgery. All results were maintained at 12 months. A reduction in the kyphotic angulation was observed postoperatively (−5.4 ± 6.3°; p < 0.001), remained at 12 months (−4.4 ± 6.0°, p = 0.002). No adverse events were implant-related and none required device removal. Three patients (2.9%) experienced procedure-related complications. The overall adjacent fracture rate up to 1 year after surgery was 2.9%. The SpineJack procedure is an effective, low-risk procedure for patients with traumatic VCF allowing a fast and sustained improvement in quality of life over 1 year after surgery.

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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Background: Decompression of the lumbar spine is one of the most common procedures performed in spine surgery. Hospital length of stay (LOS) is a clinically relevant metric used to assess surgical success, patient outcomes, and socioeconomic impact. This study aimed to investigate a variety of machine learning and deep learning algorithms to reliably predict whether a patient undergoing decompression of lumbar spinal stenosis will experience a prolonged LOS. Methods: Patients undergoing treatment for lumbar spinal stenosis with microsurgical and full-endoscopic decompression were selected within this retrospective monocentric cohort study. Prolonged LOS was defined as an LOS greater than or equal to the 75th percentile of the cohort (normal versus prolonged stay; binary classification task). Unsupervised learning with K-means clustering was used to find clusters in the data. Hospital stay classes were predicted with logistic regression, RandomForest classifier, stochastic gradient descent (SGD) classifier, K-nearest neighbors, Decision Tree classifier, Gaussian Naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), multilayer perceptron artificial neural network (MLP), and radial basis function neural network (RBNN) in Python. Prediction accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. Further, we developed a decision tree based on the Chi-square automatic interaction detection (CHAID) algorithm to investigate cut-offs of predictors for clinical decision-making. Results: 236 patients and 14 feature variables were included. K-means clustering separated data into two clusters distinguishing the data into two patient risk characteristic groups. The algorithms reached AUCs between 67.5% and 87.3% for the classification of LOS classes. Feature importance analysis of deep learning algorithms indicated that operation time was the most important feature in predicting LOS. A decision tree based on CHAID could predict 84.7% of the cases. Conclusions: Machine learning and deep learning algorithms can predict whether patients will experience an increased LOS following lumbar decompression surgery. Therefore, medical resources can be more appropriately allocated to patients who are at risk of prolonged LOS.