Study Design: Narrative review. Objectives: This review aims to present current applications of machine learning (ML) in spine domain to clinicians. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. Results: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. Conclusions: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models’ assistance in real work.

Study Design Retrospective study. Objectives To develop machine learning (ML) models to predict recurrent lumbar disc herniation (rLDH) following percutaneous endoscopic lumbar discectomy (PELD). Methods We retrospectively analyzed 1159 patients who had undergone single-level PELD for lumbar disc herniation (LDH) between July 2014 to December 2019 at our institution. Various preoperative imaging variables and demographic metrics were brought in analysis. Student’s t test and Chi-squared test were applied for univariate analysis, which were feature selection for ML models. We established ML models to predict rLDH: Artificial neural networks (ANN), Extreme Gradient Boost classifier (XGBoost), KNeighborsClassifier (KNN), Decision tree classifier (Decision Tree), Random forest classifier (Random Forest), and support vector classifier (SVC). Results A total 130 patients (11.22%) were diagnosed as rLDH in 1159 patients. Recurrence occurred within 10.25 ± 11.05 months. Body mass index (BMI) ( P = .027), facet orientation (FO) ( P < .001), herniation type ( P = .012), Modic changes ( P = .004), and disc calcification ( P = .013) are significant factors in univariate analysis ( P < .05). Extreme Gradient Boost classifier, Random Forest, ANN showed fine area under the curve, .9315, .9220, and .8814 respectively. Conclusion We developed a deep learning and 2 ensemble models with fine performance in prediction of rLDH following PELD. Predicting re-herniation before surgery has the potential to optimize decision-making and meaningfully decrease the rates of rLDH following PELD. Our ML model identified higher BMI, lower FO, Modic changes, disc calcification in a non-protrusive region, and herniation type (noncontained herniation) as significant features for predicting rLDH.

OBJECTIVE The purpose of this study was to develop natural language processing (NLP)–based machine learning algorithms to automatically differentiate lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) based on positive symptoms in free-text admission notes. The secondary purpose was to compare the performance of the deep learning algorithm with the ensemble model on the current task. METHODS In total, 1921 patients whose principal diagnosis was LDH or LSS between June 2013 and June 2020 at Zhongda Hospital, affiliated with Southeast University, were retrospectively analyzed. The data set was randomly divided into a training set and testing set at a 7:3 ratio. Long Short-Term Memory (LSTM) and extreme gradient boosting (XGBoost) models were developed in this study. NLP algorithms were assessed on the testing set by the following metrics: receiver operating characteristic (ROC) curve, area under the curve (AUC), accuracy score, recall score, F1 score, and precision score. RESULTS In the testing set, the LSTM model achieved an AUC of 0.8487, accuracy score of 0.7818, recall score of 0.9045, F1 score of 0.8108, and precision score of 0.7347. In comparison, the XGBoost model achieved an AUC of 0.7565, accuracy score of 0.6961, recall score of 0.7387, F1 score of 0.7153, and precision score of 0.6934. CONCLUSIONS NLP-based machine learning algorithms were a promising auxiliary to the electronic health record in spine disease diagnosis. LSTM, the deep learning model, showed better capacity compared with the widely used ensemble model, XGBoost, in differentiation of LDH and LSS using positive symptoms. This study presents a proof of concept for the application of NLP in prediagnosis of spine disease.

Objective. To investigate whether lumbosacral transitional vertebrae (LSTV) affects the clinical outcomes of percutaneous endoscopic lumbar discectomy (PELD) in adolescent patients with lumbar disc herniation (LDH). Methods. This was a retrospective study with two groups. Group A was made up of 22 adolescent LDH patients with LSTV (18 males and 4 females). Group B was made up of 44 adolescent LDH patients without LSTV (36 males and 4 females), who were matched to group A for age, sex, and body mass index. All patients underwent PELD at the L4/5 or L5/S1 single level and were followed up at 18 months after surgery. We identified LSTV on radiographs and computed tomography and assessed the imaging characteristics of all patients. Outcomes were evaluated through a numerical rating scale (NRS), the Oswestry Disability Index (ODI), the modified MacNab grading system, and the incidence of additional lumbar surgery. Results. At 18 months after PELD, both groups had significant improvements in the mean NRS scores of low back pain (LBP) or leg pain and the ODI scores. In terms of the MacNab criteria, 90.9% in group A and 93.2% in group B showed excellent or good outcomes. The mean NRS scores of LBP or leg pain, ODI score, and MacNab grade after surgery were not significantly different between the 2 groups. Two patients (one patient had a recurrence; one patient had a new lumbar disc herniation) in group A and 3 patients (one patient had a recurrence; two patients had new lumbar disc herniations) in group B underwent additional lumbar surgery. Conclusions. Our study suggests that in terms of pain relief, life function improvement, and the incidence of additional lumbar surgery, LSTV has no effect on the short-term clinical outcomes of PELD in adolescents. A new lumbar disc herniation is an important reason for additional surgery in adolescents, regardless of the LSTV status.

Background: Ideal tools should not only investigate risk factors, but also provide explicit auxiliary answer for whether a patient will develop surgical site infection (SSI) or not. Machine learning (ML) models have ability to carry out complicated predictive medical tasks. We intend to develop ML models to predict SSI after posterior cervical surgery and interpret the outcome. Methods: We retrospectively analyzed 235 patients who had undergone posterior cervical surgery between June 2013 to April 2019 at Zhongda Hospital Affiliated to Southeast University. We established Artificial neural networks (ANN), XGBClassifier (xgboost), KNeighborsClassifier (KNN), Decision tree classifier (decision tree), Random forest classifier (random forest) and support vector classifier (SVC). Receiver operating characteristic (ROC) curve, area under the curve (AUC) score, accuracy score, recall score, F1 score and precision score were calculated to measure models’ performance. Shapley values were calculated using SHapley Additive exPlanations (SHAP) to determine relative feature importance of xgboost model. Results: The incidence of SSI was 7.23%. With AUC of 0.9972, 0.9923, 0.9865, 0.9615, 0.9540, 0.8934, the xgboost, random forest, ANN, KNN, decision tree, SCV accurately predicted SSI. Xgboost, ANN, decision tree and random forest achieved excellent performance in testing set. Top 10 variables with high predictive contribution of xgboost including, drainage volume, body mass index (BMI), drainage duration, operation blooding, cholesterin, sex, prognostic nutritional index (PNI), albumin, hypertension, operation time. Conclusion: We had successful established ML models in individualized predicting SSI after posterior cervical surgery. Xgboost, ANN, decision tree and random forest achieved excellent performance which could provide auxiliary information for clinical decision makers. The interpretable model focuses on contribution of important features to the predictive result. It can improve the acceptance of clinicians on ML and promote ML’s application in the actual clinical work.