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Machine Learning Predicts Recurrent Lumbar Disc Herniation Following Percutaneous Endoscopic Lumbar Discectomy
| Content Provider | SAGE Publishing |
|---|---|
| Author | Ren, GuanRui Liu, Lei Zhang, Po Xie, ZhiYang Wang, PeiYang Zhang, Wei Wang, Hui Shen, MeiJi Deng, LiTing Tao, YuAo Li, Xi Wang, JiaoDong Wang, YunTao Wu, XiaoTao |
| Copyright Year | 2022 |
| Abstract | Study DesignRetrospective study.ObjectivesTo develop machine learning (ML) models to predict recurrent lumbar disc herniation (rLDH) following percutaneous endoscopic lumbar discectomy (PELD).MethodsWe 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).ResultsA 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.ConclusionWe 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. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/21925682221097650?download=true |
| ISSN | 21925682 |
| Journal | Global Spine Journal (GSJ) |
| e-ISSN | 21925690 |
| DOI | 10.1177/21925682221097650 |
| Language | English |
| Publisher | Sage Publications CA |
| Publisher Date | 2022-05-02 |
| Publisher Place | Los Angeles |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2022 |
| Subject Keyword | recurrent lumbar disc herniation machine learning percutaneous endoscopic lumbar discectomy deep learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Orthopedics and Sports Medicine Neurology (clinical) Surgery |
