ArticleScientific reports2021
Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions.Neurosurgical review · 2026Pooled it
- Artificial intelligence automated measurements of spinopelvic parameters in adult spinal deformity-a systematic review.Spine deformity · 2025Pooled it
- Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.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 · 2022Pooled it
- Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.Journal of healthcare engineering · 2022Trial
- Automatic Body Region Classification in CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based automatic measurement of the femoral head ossification center in healthy Korean children: development of a novel radiographic growth chart.European radiology · 2026Article
- Automated measurement of spinopelvic alignment parameters using a spine planning software: a validation study.Spine deformity · 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
- A deep learning framework for the localization of landmarks on the lateral semi circular canals.PloS one · 2026Article
- Controlled comparative study of YOLOv8-Pose, YOLOv11-Pose, and Detectron2 for vertebrae detection and keypoint estimation.PloS one · 2026Article
- Implementation of artificial intelligence (AI) in ASD treatment.North American Spine Society journal · 2025Article
- Clinical validation of a deep learning tool for characterizing spinopelvic mobility in total hip arthroplasty.Scientific reports · 2025Article
- Lumbar region perception segmentation: a novel algorithm for automated vertebral motion parameter measurement.Quantitative imaging in medicine and surgery · 2025Article
- Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.Bioengineering (Basel, Switzerland) · 2025Review
- Research on a deep learning-based model for measurement of X-ray imaging parameters of atlantoaxial joint.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 · 2025Article
- Review
- Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review.European journal of medical research · 2025Review
- Automated radiographic metrics for diagnosing lumbar spine instability: a cross-sectional observational study.Quantitative imaging in medicine and surgery · 2025Article
- Anatomical landmark detection on bi-planar radiographs for predicting spinopelvic parameters.Spine deformity · 2025Article
- Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset.Global spine journal · 2025Article
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6 authors.
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Abstract
Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.
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