ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2019
Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays.
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, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 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
- Enhancing Medical Students' Disease Detection Skills: Leveraging Artificial Intelligence Software for Diagnosing Developmental Dysplasia Using Ultrasound Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Global research trends and hotspots in robot-assisted spinal surgery for spinal deformity: a bibliometric analysis.Journal of robotic surgery · 2026Article
- Automated scoliosis X-ray cobb angle classification: a deep learning approach with RadImageNet.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
- 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 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
- Three-dimensional markerless surface topography approach with convolutional neural networks for adolescent idiopathic scoliosis screening.Scientific reports · 2025Article
- A deep learning model for radiological measurement of adolescent idiopathic scoliosis using biplanar radiographs.Journal of orthopaedic surgery and research · 2025Article
- Automated weight-bearing foot measurements using an artificial intelligence-based software.Skeletal radiology · 2025Article
- PCdare software registers 3D back surface with biplanar radiographs: application to patients with scoliosis.Frontiers in digital health · 2025Article
- Article
- A 20-year research trend analysis of the artificial intelligence on scoliosis using bibliometric methods.Frontiers in pediatrics · 2025Review
- Evaluation of a deep learning software for automated measurements on full-leg standing radiographs.Knee surgery & related research · 2024Article
- 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
- Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI.European radiology · 2024Article
- Radiographic Analysis of Scoliosis Using Convolutional Neural Network in Clinical Practice.Journal of the Korean Society of Radiology · 2024Article
- Fully Automated Measurement of Cobb Angles in Coronal Plane Spine Radiographs.Journal of clinical medicine · 2024Article
- Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays.Global spine journal · 2024Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
objectivesTo automatically measure the Cobb angle and diagnose scoliosis on chest X-rays, a computer-aided method was proposed and the reliability and accuracy were evaluated.
methodsTwo Mask R-CNN models as the core of a computer-aided method were used to separately detect and segment the spine and all vertebral bodies on chest X-rays, and the Cobb angle of the spinal curve was measured from the output of the Mask R-CNN models. To evaluate the reliability and accuracy of the computer-aided method, the Cobb angles on 248 chest X-rays from lung cancer screening were measured automatically using a computer-aided method, and two experienced radiologists used a manual method to separately measure Cobb angles on the aforementioned chest X-rays.
resultsFor manual measurement of the Cobb angle on chest X-rays, the intraclass correlation coefficients (ICC) of intra- and inter-observer reliability analysis was 0.941 and 0.887, respectively, and the mean absolute differences were < 3.5°. The ICC between the computer-aided and manual methods for Cobb angle measurement was 0.854, and the mean absolute difference was 3.32°. These results indicated that the computer-aided method had good reliability for Cobb angle measurement on chest X-rays. Using the mean value of Cobb angles in manual measurements > 10° as a reference standard for scoliosis, the computer-aided method achieved a high level of sensitivity (89.59%) and a relatively low level of specificity (70.37%) for diagnosing scoliosis on chest X-rays.
conclusionThe computer-aided method has potential for automatic Cobb angle measurement and scoliosis diagnosis on chest X-rays. These slides can be retrieved under Electronic Supplementary Material.
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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.