ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2024
Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.
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, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence automated measurements of spinopelvic parameters in adult spinal deformity-a systematic review.Spine deformity · 2025Pooled it
- Multidimensional Management of Degenerative Scoliosis: A Comprehensive Narrative Review Integrating Diagnostic Assessment, Surgical Decision-Making, and Patient-Centered Outcomes.Orthopaedic surgery · 2026Review
- [AI-based surgical planning of adult spinal deformities : Analysis of an AI-based tool for correction planning and execution employing patient-specific contoured rods].Orthopadie (Heidelberg, Germany) · 2026Review
- Automated measurement of spinopelvic alignment parameters using a spine planning software: a validation study.Spine deformity · 2026Article
- Hierarchical deep learning pipeline for robust cervical parameter measurement in radiographs with C7 obscuration.NPJ digital medicine · 2026Article
- CT Evaluation of Lumbar Interbody Fusion: A Comprehensive Review with an Integrated Framework for Principle-Based Interpretation.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Simulating Federated Learning to Enable Multi-Hospital Collaboration for Lumbopelvic Alignment Estimation.JOR spine · 2025Article
- Clinical validation of a deep learning tool for characterizing spinopelvic mobility in total hip arthroplasty.Scientific reports · 2025Article
- Association of Spinopelvic Anatomy with the Level of Lumbar Disc Herniation.Bioengineering (Basel, Switzerland) · 2025Article
- Advancing spine care through AI and machine learning: overview and applications.EFORT open reviews · 2024Review
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
aimDeep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD). MATERIAL AND
methodsSagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements.
resultsThe DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71-0.99 for preoperative and 0.72-0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively.
conclusionThis is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.
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