ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2022
Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology.
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, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05392504 (Effects of Core Stabilization Exercises Versus Schroth's Program on Posture and Quality of Life in Females With Idiopathic Scoliosis.), which is not on this map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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.
Effects of Core Stabilization Exercises Versus Schroth's Program on Posture and Quality of Life in Females With Idiopathic Scoliosis.
Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.Spine deformity · 2025Pooled it
- Fully Automated Deep Learning-Based Lenke Classification for Adolescent Idiopathic Scoliosis Using Multi-View Full-Spine Radiographs: Development and Clinical Validation.Global spine journal · 2026Article
- Dual-stage anatomical perception and reasoning framework for automated cobb angle estimation.Medical & biological engineering & computing · 2026Article
- Posture-dependent assessment of whole-spine curvature with a rotatable cryogen-free 1.5T MRI and automated Cobb angle analysis.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
- Current applications and challenges of artificial intelligence applied to diagnostics in pediatric musculoskeletal imaging.Pediatric radiology · 2026Review
- Geometric foundations of measurement uncertainty and clinical relevance in radiographic spinal angle assessment.Spine deformity · 2026Article
- Automated Measurement of Occipito-Axial Angle on Cervical Radiographs Using a Deep Learning Object Detection Model: A Proof-of-Concept Study.Neurospine · 2026Article
- Performance of a deep learning-based algorithm for automated measurements of Cobb angles on preoperative spine radiographs in adolescent idiopathic scoliosis.Pediatric radiology · 2026Article
- Deep learning based screening and regular assessment of adolescent idiopathic scoliosis using wearable IMU sensors.Frontiers in bioengineering and biotechnology · 2026Article
- Interpretable automated cobb angle measurement in adolescent idiopathic scoliosis using a lightweight keypoint-to-geometry network: cross-center external validation.Frontiers in medicine · 2026Article
- Reliability of Large Language Model-Based Artificial Intelligence in AIS Assessment: Lenke Classification and Fusion-Level Suggestion.Diagnostics (Basel, Switzerland) · 2025Article
- A novel artificial Intelligence-Based model for automated Lenke classification in adolescent idiopathic scoliosis.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
- Deep learning algorithm enables automated Cobb angle measurements with high accuracy.Skeletal radiology · 2025Article
- Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN.Diagnostics (Basel, Switzerland) · 2025Article
- A deep learning model for radiological measurement of adolescent idiopathic scoliosis using biplanar radiographs.Journal of orthopaedic surgery and research · 2025Article
- Deep learning automates Cobb angle measurement compared with multi-expert observers.BJR artificial intelligence · 2025Article
- Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis.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 · 2024Review
- Repeatability and intra and interrater reproducibility of the DIPA-S eHealth© capture and analysis system for clinical assessment of scoliosis.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 · 2024Article
- Radiographic Analysis of Scoliosis Using Convolutional Neural Network in Clinical Practice.Journal of the Korean Society of Radiology · 2024Article
- Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe present study compared manual and automated measurement of Cobb angle in idiopathic scoliosis based on deep learning keypoint detection technology.
methodsA total of 181 anterior-posterior spinal X-rays were included in this study, including 165 cases of idiopathic scoliosis and 16 normal adult cases without scoliosis. We labeled all images and randomly chose 145 as the training set and 36 as the test set. Two state-of-the-art deep learning object detection models based on convolutional neural networks were used in sequence to segment each vertebra and locate the vertebral corners. Cobb angles measured from the output of the models were compared to manual measurements performed by orthopedic experts.
resultsThe mean Cobb angle in test cases was 27.4° ± 19.2° (range 0.00-91.00°) with manual measurements and 26.4° ± 18.9° (range 0.00-88.00°) with automated measurements. The automated method needed 4.45 s on average to measure each radiograph. The intra-class correlation coefficient (ICC) for the reliability of the automated measurement of the Cobb angle was 0.994. The Pearson correlation coefficient and mean absolute error between automated positioning and expert annotation were 0.990 and 2.2° ± 2.0°, respectively. The analytical result for the Spearman rank-order correlation was 0.984 (p < 0.001).
conclusionThe automated measurement results agreed with the experts' annotation and had a high degree of reliability when the Cobb angle did not exceed 90° and could locate multiple curves in the same scoliosis case simultaneously in a short period of time. Our results need to be verified in more cases in the future.
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