ArticleInternational journal of computer assisted radiology and surgery2020
A convolutional neural network to detect scoliosis treatment in radiographs.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Deep learning based vertebra localization and Cobb angle estimation using x-ray images.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
- Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence.Spine deformity · 2024Review
- The Application of Surface Electromyography Technology in Evaluating Paraspinal Muscle Function.Diagnostics (Basel, Switzerland) · 2024Review
- Prediction of Fusion Rod Curvature Angles in Posterior Scoliosis Correction Using Artificial Intelligence.The archives of bone and joint surgery · 2024Article
- Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology.Pediatric radiology · 2022Review
- Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs.Medicina (Kaunas, Lithuania) · 2022Article
- Electromyographic Analysis of Paraspinal Muscles of Scoliosis Patients Using Machine Learning Approaches.International journal of environmental research and public health · 2022Article
- Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images.PloS one · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this work is to propose a classification algorithm to automatically detect treatment for scoliosis (brace, implant or no treatment) in postero-anterior radiographs. Such automatic labelling of radiographs could represent a step towards global automatic radiological analysis.
methodsSeven hundred and ninety-six frontal radiographies of adolescents were collected (84 patients wearing a brace, 325 with a spinal implant and 387 reference images with no treatment). The dataset was augmented to a total of 2096 images. A classification model was built, composed by a forward convolutional neural network (CNN) followed by a discriminant analysis; the output was a probability for a given image to contain a brace, a spinal implant or none. The model was validated with a stratified tenfold cross-validation procedure. Performance was estimated by calculating the average accuracy.
results98.3% of the radiographs were correctly classified as either reference, brace or implant, excluding 2.0% unclassified images. 99.7% of brace radiographs were correctly detected, while most of the errors occurred in the reference group (i.e. 2.1% of reference images were wrongly classified).
conclusionThe proposed classification model, the originality of which is the coupling of a CNN with discriminant analysis, can be used to automatically label radiographs for the presence of scoliosis treatment. This information is usually missing from DICOM metadata, so such method could facilitate the use of large databases. Furthermore, the same model architecture could potentially be applied for other radiograph classifications, such as sex and presence of scoliotic deformity.
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