ArticleJOR spine2024
Automatic Lenke classification of adolescent idiopathic scoliosis with deep learning.
Article in JOR spine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
- Accuracy of general-purpose large language models (LLMS) in interpreting adolescent idiopathic scoliosis radiographs: a comparative study with experienced spine surgeons.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
- 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
- Automating Adolescent Idiopathic Scoliosis Classification: An Interpretable, Workflow-Based Approach to Lenke Classification: Commentary on an article by Lingcong Xu, MD, et al.: " A Fully Automated Multistage Deep Learning System for Lenke Classification. Enhanced Diagnostic Precision in Adolescent Idiopathic Scoliosis ".The Journal of bone and joint surgery. American volume · 2026Article
- A Fully Automated Multistage Deep Learning System for Lenke Classification: Enhanced Diagnostic Precision in Adolescent Idiopathic Scoliosis.The Journal of bone and joint surgery. American volume · 2026Article
- Deep Learning-Assisted Lumbar Degeneration Evaluation Using Plain Radiographs: Development and Validation of a Novel Dual-Mechanism Architecture.Neurospine · 2026Article
- Prediction of Cobb angle progression in female patients with mild-to-moderate 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 · 2026Article
- Deep learning-based lightweight model for automated lumbar foraminal stenosis classification: sagittal CT diagnostic performance compared to clinical subspecialists.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
- Automatic measurement of Caton index on knee X-ray images using a key point detection model.European journal of radiology open · 2025Article
- Article
- 2D and 3D Classification Systems for Adolescent Idiopathic Scoliosis: Clinical Implications and Technological Advances.Orthopaedic surgery · 2025Review
- Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.Scientific reports · 2025Article
- Superior performance of a center-point AI model over VFLDNet in automated cobb angle estimation for scoliosis assessment.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
- Deep Learning-Based Prediction Model for the Cobb Angle in Adolescent Idiopathic Scoliosis Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Automatic Lenke classification of adolescent idiopathic scoliosis with deep learning.JOR spine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The Lenke classification system is widely utilized as the preoperative evaluation protocol for adolescent idiopathic scoliosis (AIS). However, manual measurement is susceptible to observer-induced variability, which consequently impacts the evaluation of progression. The goal of this investigation was to develop an automated Lenke classification system utilizing innovative deep learning algorithms. Methods: Using the database from the First Affiliated Hospital of Sun Yat-sen University, the whole spinal x-rays images were retrospectively collected. Specifically, images collection was divided into AIS and control group. The control group consisted of individuals who underwent routine health checks and did not have scoliosis. Afterwards, relative features of all images were annotated. Deep learning was implemented through the utilization of the key-point based detection method to realize the vertebral detection, and Cobb angle measurement and scoliosis classification were performed based on relevant standards. Besides, the segmentation method was employed to achieve the recognition of lumbar vertebral pedicle to determine the type of lumbar spine modifier. Finally, the model performance was further quantitatively analyzed. Results: In the study, a total of 2082 spinal x-ray images were collected from 407 AIS patients and 227 individuals in the control group. The model for vertebral detection achieved an F1-score of 0.809 for curve type evaluation and an F1-score of 0.901 for thoracic sagittal profile. The intraclass correlation efficient (ICC) of the Cobb angle measurement was 0.925. In the analysis of performance for vertebra pedicle segmentation model, the F1-score of lumbar modification profile was 0.942, the intersection over union (IOU) of the target pixels was 0.827, and the Hausdorff distance (HD) was 6.565 ± 2.583 mm. Specifically, the F1-score for ultimate Lenke type classifier was 0.885. Conclusions: This study has constructed an automated Lenke classification system by employing the deep learning networks to achieve the recognition pattern and feature extraction. Our models require further validation in additional cases in the future.
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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.