ArticleScientific reports2023
Automatic measurement of the Cobb angle for adolescent idiopathic scoliosis using convolutional neural network.
Article in Scientific reports, 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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Who cites it
21 citing papers in PubMed, 19 citations in OpenAlex.
- 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
- From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.Journal of clinical medicine · 2026Review
- Principles Underlying Surgical Decision-Making in Lowest-Instrumented Vertebra Level Selection of Adolescent Idiopathic Scoliosis: From Traditional Landmarks to Emerging Predictive Models-A Narrative Review.Journal of clinical medicine · 2026Review
- Radiation-free Cobb angle estimation in adolescent scoliosis using surface topography and a linear regression model.Spine deformity · 2026Article
- Automated quantification of coronal balance in spinal deformity: a safety-aware clinical workflow.Scientific reports · 2026Article
- Automated Measurement of Occipito-Axial Angle on Cervical Radiographs Using a Deep Learning Object Detection Model: A Proof-of-Concept Study.Neurospine · 2026Article
- Vertebra-Level Completeness Analysis in Thoracolumbar Ultrasound Using a YOLO-Based Detection Framework.Sensors (Basel, Switzerland) · 2026Article
- Deep learning based screening and regular assessment of adolescent idiopathic scoliosis using wearable IMU sensors.Frontiers in bioengineering and biotechnology · 2026Article
- Development of a multimodal vision transformer model for predicting traumatic versus degenerative rotator cuff tears on magnetic resonance imaging: A single-centre retrospective study.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Development and validation of a keypoint region-based convolutional neural network to automate thoracic Cobb angle measurements using whole-spine standing radiographs.Acta neurochirurgica · 2025Article
- Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation.World journal of orthopedics · 2025Review
- Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Deep learning algorithm enables automated Cobb angle measurements with high accuracy.Skeletal radiology · 2025Article
- An end-to-end pipeline for automated scoliosis diagnosis with standardized clinical reporting using SNOMED CT.Scientific reports · 2025Article
- Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN.Diagnostics (Basel, Switzerland) · 2025Article
- Intelligent measurement of adolescent idiopathic scoliosis x-ray coronal imaging parameters based on VB-Net neural network: a retrospective analysis of 2092 cases.Journal of orthopaedic surgery and research · 2025Article
- PCdare software registers 3D back surface with biplanar radiographs: application to patients with scoliosis.Frontiers in digital health · 2025Article
- A 20-year research trend analysis of the artificial intelligence on scoliosis using bibliometric methods.Frontiers in pediatrics · 2025Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes a convolutional neural network method for automatic vertebrae detection and Cobb angle (CA) measurement on X-ray images for scoliosis. 1021 full-length X-ray images of the whole spine of patients with adolescent idiopathic scoliosis (AIS) were used for training and segmentation. The proposed AI algorithm's results were compared with those of the manual method by six doctors using the intraclass correlation coefficient (ICC). The ICCs recorded by six doctors and AI were excellent or good, with a value of 0.973 for the major curve in the standing position. The mean error between AI and doctors was not affected by the angle size, with AI tending to measure 1.7°-2.2° smaller than that measured by the doctors. The proposed method showed a high correlation with the doctors' measurements, regardless of the CA size, doctors' experience, and patient posture. The proposed method showed excellent reliability, indicating that it is a promising automated method for measuring CA in patients with AIS.
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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.