ArticleNeurospine2023
Predicting Mechanical Complications After Adult Spinal Deformity Operation Using a Machine Learning Based on Modified Global Alignment and Proportion Scoring With Body Mass Index and Bone Mineral Density.
Article in Neurospine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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.
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Who cites it
21 citing papers in PubMed.
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Not just alignment: body shape influences adult spinal deformity biomechanics.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 · 2026Article
- Development and validation of a deep learning-powered system for multi-version global alignment and proportion score to predict mechanical complications after adult degenerative scoliosis surgery.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 · 2026Article
- Determining the risk factors for postoperative mechanical complication in degenerative scoliosis: a machine learning approach based on musculoskeletal metrics.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 · 2026Article
- The predictive value of the global alignment and proportion (GAP) score for mechanical complications following adult spinal deformity surgery: A systematic review and meta-analysis.North American Spine Society journal · 2025Review
- Predicting proximal junctional failure in adult spinal deformity patients using machine learning models based on spinal alignment parameters.Scientific reports · 2025Article
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- Article
- Classification of lumbar spine disorders using large language models and MRI segmentation.BMC medical informatics and decision making · 2024Article
- Research of Global Tilt and Functional Independence: Insights into Spinal Health of Older Women.Bioengineering (Basel, Switzerland) · 2024Article
- Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
- Commentary on "Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery".Neurospine · 2024Article
- Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery.Journal of clinical medicine · 2024Article
- Article
- Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning.Neurospine · 2023Article
- Article
- Article
- Proximal Junctional Kyphosis or Failure After Adult Spinal Deformity Surgery - Review of Risk Factors and Its Prevention.Neurospine · 2023Article
- Complication Rates and Utilization Trends of 3-Level Posterior Column Osteotomy Compared to Single-Level Pedicle Subtraction Osteotomy.Neurospine · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to create an ideal machine learning model to predict mechanical complications in adult spinal deformity (ASD) surgery based on GAPB (modified global alignment and proportion scoring with body mass index and bone mineral density) factors.
methodsBetween January 2009 and December 2018, 238 consecutive patients with ASD, who received at least 4-level fusions and were followed-up for ≥ 2 years, were included in the study. The data were stratified into training (n = 167, 70%) and test (n = 71, 30%) sets and input to machine learning algorithms, including logistic regression, random forest gradient boosting system, and deep neural network.
resultsBody mass index, bone mineral density, the relative pelvic version score, the relative lumbar lordosis score, and the relative sagittal alignment score of the global alignment and proportion score were significantly different in the training and test sets (p < 0.05) between the complication and no complication groups. In the training set, the area under receiver operating characteristics (AUROCs) for logistic regression, gradient boosting, random forest, and deep neural network were 0.871 (0.817-0.925), 0.942 (0.911-0.974), 1.000 (1.000-1.000), and 0.947 (0.915-0.980), respectively, and the accuracies were 0.784 (0.722-0.847), 0.868 (0.817-0.920), 1.000 (1.000-1.000), and 0.856 (0.803-0.909), respectively. In the test set, the AUROCs were 0.785 (0.678-0.893), 0.808 (0.702-0.914), 0.810 (0.710-0.910), and 0.730 (0.610-0.850), respectively, and the accuracies were 0.732 (0.629-0.835), 0.718 (0.614-0.823), 0.732 (0.629-0.835), and 0.620 (0.507-0.733), respectively. The random forest achieved the best predictive performance on the training and test dataset.
conclusionThis study created a comprehensive model to predict mechanical complications after ASD surgery. The best prediction accuracy was 73.2% for predicting mechanical complications after ASD surgery.
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Registered trials
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.