ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2021
Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the "RAD" risk profile.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06833099 (The Prediction of Recurrence Lumbar Disc Herniation At L5-S1 Level Through Machine Learning Models Based on Endoscopic Discectomy Via the Interlaminar Approach), which is not on this map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Prediction of Recurrence Lumbar Disc Herniation At L5-S1 Level Through Machine Learning Models Based on Endoscopic Discectomy Via the Interlaminar Approach
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Use of Machine Learning and Imaging to Predict Outcomes After Lumbar Spine Surgery: A Systematic Review.Journal of imaging informatics in medicine · 2026Review
- A single-source preoperative prediction model for recurrent lumbar disc herniation after primary discectomy: Development and internal validation in the SPORT registry.North American Spine Society journal · 2026Article
- Poor predictive performance of early outcome models following lumbar disc herniation surgery.Bone & joint open · 2026Article
- [Artificial intelligence in spinal surgery: current applications and future perspectives].Orthopadie (Heidelberg, Germany) · 2026Review
- A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy.Global spine journal · 2024Article
- Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Lumbar lordosis and sacral slope do not differ in two types of postoperative lumbar disc re-herniation: a cross-sectional observational study.BMC musculoskeletal disorders · 2024Observational
- Deep-Learning-Based Radiomics to Predict Surgical Risk Factors for Lumbar Disc Herniation in Young Patients: A Multicenter Study.Journal of multidisciplinary healthcare · 2024Article
- Computational Modeling, Augmented Reality, and Artificial Intelligence in Spine Surgery.Advances in experimental medicine and biology · 2024Review
- Machine Learning Predicts Recurrent Lumbar Disc Herniation Following Percutaneous Endoscopic Lumbar Discectomy.Global spine journal · 2024Article
- Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.International journal of computer assisted radiology and surgery · 2023Article
- An investigation of machine learning algorithms for prediction of lumbar disc herniation.Medical & biological engineering & computing · 2023Article
- Artificial Intelligence and Machine Learning Applications in Spine Surgery.International journal of spine surgery · 2023Article
- Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence in spine care: current applications and future utility.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- Artificial intelligence and spine imaging: limitations, regulatory issues and future direction.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSurgical treatment of herniated lumbar intervertebral disks is a common procedure worldwide. However, recurrent herniated nucleus pulposus (re-HNP) may develop, complicating outcomes and patient management. The purpose of this study was to utilize machine-learning (ML) analytics to predict lumbar re-HNP, whereby a personalized risk prediction can be developed as a clinical tool.
methodsA retrospective, single center study was conducted of 2630 consecutive patients that underwent lumbar microdiscectomy (mean follow-up: 22-months). Various preoperative patient pain/disability/functional profiles, imaging parameters, and anthropomorphic/demographic metrics were noted. An Extreme Gradient Boost (XGBoost) classifier was implemented to develop a predictive model identifying patients at risk for re-HNP. The model was exported to a web application software for clinical utility.
resultsThere were 1608 males and 1022 females, 114 of whom experienced re-HNP. Primary herniations were central (65.8%), paracentral (17.6%), and far lateral (17.1%). The XGBoost algorithm identified multiple re-HNP predictors and was incorporated into an open-access web application software, identifying patients at low or high risk for re-HNP. Preoperative VAS leg, disability, alignment parameters, elevated body mass index, symptom duration, and age were the strongest predictors.
conclusionsOur predictive modeling via an ML approach of our large-scale cohort is the first study, to our knowledge, that has identified significant risk factors for the development of re-HNP after initial lumbar decompression. We developed the re-herniation after decompression (RAD) profile index that has been translated into an online screening tool to identify low-high risk patients for re-HNP. Additional validation is needed for potential global implementation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.