ArticlePloS one2021
Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis.
Article in PloS one, 2021. 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.
- 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
- Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution.Spine deformity · 2026Observational
- Extending convolutional neural networks to detect differences in symmetry in videorasterstereographic back scans with the aim to improve screening for 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
- Reference ranges and dimensional structure of upper body posture parameters in adults: a cross-sectional study.Frontiers in rehabilitation sciences · 2026Article
- Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes.Healthcare (Basel, Switzerland) · 2025Review
- Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review.European journal of medical research · 2025Review
- Deep learning prediction of curve severity from rasterstereographic back images 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 · 2024Observational
- Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images.PloS one · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of our study was to classify scoliosis compared to to healthy patients using non-invasive surface acquisition via Video-raster-stereography, without prior knowledge of radiographic data. Data acquisitions were made using Rasterstereography; unsupervised learning was adopted for clustering and supervised learning was used for prediction model Support Vector Machine and Deep Network architectures were compared. A M-fold cross validation procedure was performed to evaluate the results. The accuracy and balanced accuracy of the best supervised model were close to 85%. Classification rates by class were measured using the confusion matrix, giving a low percentage of unclassified patients. Rasterstereography has turned out to be a good tool to distinguish subject with scoliosis from healthy patients limiting the exposure to unnecessary radiations.
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Registered trials
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.