Natural Language Processing-Driven Artificial Intelligence Models for the Diagnosis of Lumbar Disc Herniation with L5 and S1 Radiculopathy: A Preliminary Evaluation.

OBJECTIVE: To develop and validate natural language processing-driven artificial intelligence (AI) models for the diagnosis of lumbar disc herniation (LDH) with L5 and S1 radiculopathy using electronic health records (EHRs). METHODS: EHRs of patients undergoing single-level percutaneous endoscopic lumbar discectomy for the treatment of LDH at the L4/5 or L5/S1 level between June 1, 2013, and December 31, 2021, were collected. The primary outcome was LDH with L5 and S1 radiculopathy, which was defined as nerve root compression recorded in the operative notes. Datasets were created using the history of present illness text and positive symptom text with radiculopathy (L5 or S1), respectively. The datasets were randomly split into a training set and a testing set in a 7:3 ratio. Two machine learning models, the long short-term memory network and Extreme Gradient Boosting, were developed using the training set. Performance evaluation of the models on the testing set was done using measures such as the receiver operating characteristic curve, area under the curve, accuracy, recall, F1-score, and precision. RESULTS: The study included a total of 1681 patients, with 590 patients having L5 radiculopathy and 1091 patients having S1 radiculopathy. Among the 4 models developed, the long short-term memory model based on positive symptom text showed the best discrimination in the testing set, with precision (0.9054), recall (0.9405), accuracy (0.8950), F1-score (0.9226), and area under the curve (0.9485). CONCLUSIONS: This study provides preliminary validation of the concept that natural language processing-driven AI models can be used for the diagnosis of lumbar disease using EHRs. This study could pave the way for future research that may develop more comprehensive and clinically impactful AI-driven diagnostic systems.

Interpretable Machine Learning Model to Predict Bone Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fracture Based on SHapley Additive exPlanations.

STUDY DESIGN: Retrospective study. OBJECTIVES: Our objective is to create comprehensible machine learning (ML) models that can forecast bone cement leakage in percutaneous vertebral augmentation (PVA) for individuals with osteoporotic vertebral compression fracture (OVCF) while also identifying the associated risk factors. METHODS: We incorporated data from patients (n = 425) which underwent PVA. To predict cement leakage, we devised six models based on a variety of parameters. Evaluate and juxtapose the predictive performances relied on measures of discrimination, calibration, and clinical utility. SHapley Additive exPlanations (SHAP) methodology was used to interpret model and evaluate the risk factors associated with cement leakage. RESULTS: The occurrence rate of cement leakage was established at 50.4%. A binary logistic regression analysis identified cortical disruption (OR 6.880, 95% CI 4.209-11.246), the basivertebral foramen sign (OR 2.142, 95% CI 1.303-3.521), the fracture type (OR 1.683, 95% CI 1.083-2.617), and the volume of bone cement (OR 1.198, 95% CI 1.070-1.341) as independent predictors of cement leakage. The XGBoost model outperformed all others in predicting cement leakage in the testing set, with AUC of .8819, accuracy of .8025, recall score of .7872, F1 score of .8315, and a precision score of .881. Several important factors related to cement leakage were drawn based on the analysis of SHAP values and their clinical significance. CONCLUSION: The ML based predictive model demonstrated significant accuracy in forecasting bone cement leakage for patients with OVCF undergoing PVA. When combined with SHAP, ML facilitated a personalized prediction and offered a visual interpretation of feature importance.

The Mini-Open Wiltse Approach with Pedicle Screw Fixation Versus Percutaneous Pedicle Screw Fixation for Treatment of Neurologically Intact Thoracolumbar Fractures: A Systematic Review and Meta-Analysis.

OBJECTIVE: The purpose of the present study was to compare the clinical outcomes and complications between the mini-open Wiltse approach with pedicle screw fixation (MWPSF) and percutaneous pedicle screw fixation (PPSF) in treating neurologically intact thoracolumbar fractures. METHODS: We comprehensively searched PubMed, Web of Science, Embase, and the Cochrane Library and performed a systematic review and meta-analysis of all randomized controlled trials and retrospective comparative studies assessing these important indexes of the 2 methods using Review Manager, version 5.4. The clinical outcomes are presented as the risk difference for dichotomous outcomes and the mean difference for continuous outcomes with the 95% confidence intervals. Heterogeneity was assessed using the χ2 test and I2 statistics. The study was registered with PROSPERO (CRD 42021290078). RESULTS: Two randomized controlled trials and six retrospective cohort studies were included in the present analysis. The percutaneous approach was associated with less intraoperative blood loss compared with the mini-open Wiltse approach. No significant differences were found in the total length of the incisions, hospitalization time, postoperative visual analog scale scores, postoperative Oswestry disability index, postoperative Cobb angle, postoperative Cobb angle correction, postoperative Cobb angle correction loss, accuracy rate of pedicle screw placement, and postoperative complications between MWPSF and PPSF. However, the incidence of facet joint violation was significantly higher in the PPSF group. In addition, MWPSF was associated with a shorter operative time, shorter intraoperative fluoroscopy time, lower hospitalization costs, better postoperative vertebral body angle and percentage of vertebral body height compared with PPSF. CONCLUSIONS: Both MWPSF and PPSF are safe and effective treatments of neurologically intact thoracolumbar fractures. Nevertheless, our results have indicated that MWPSF might be the better choice, because it has a shorter learning curve and decreased facet joint violation, operative time, hospitalization costs, and radiation exposure. In addition, MWPSF was associated with better improvement of the postoperative vertebral body angle and percentage of vertebral body height.

Differentiation of lumbar disc herniation and lumbar spinal stenosis using natural language processing-based machine learning based on positive symptoms.

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.

Current Applications of Machine Learning in Spine: From Clinical View.

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.

Lumbosacral Transitional Vertebrae in Adolescents: Effects on the Short-Term Outcomes of Percutaneous Endoscopic Lumbar Discectomy.

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.