ArticleMedical & biological engineering & computing2020
Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Evaluation of the effect of Rigo-Chêneau type brace on adolescent idiopathic scoliosis using computer-aided modeling and 3D surface analysis.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
- A scoping review on the application of new technology in the screening and diagnosis of adolescent idiopathic scoliosis.Spine deformity · 2026Review
- 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
- 3D topographic acquisitions to predict spinal curvature in adolescent idiopathic scoliosis : a prospective validation study.Bone & joint open · 2026Article
- Enhanced Vision Transformer with Custom Attention Mechanism for Automated Idiopathic Scoliosis Classification.Journal of imaging informatics in medicine · 2026Article
- Comparative analysis of a manual and an automated 3D landmark digitization method of the torso in adolescents with idiopathic scoliosis.Scientific reports · 2026Article
- Deep learning based screening and regular assessment of adolescent idiopathic scoliosis using wearable IMU sensors.Frontiers in bioengineering and biotechnology · 2026Article
- Wearable Devices in Scoliosis Treatment: A Scoping Review of Innovations and Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence.Spine deformity · 2024Review
- Current issues in the treatment of adolescent idiopathic scoliosis: a comprehensive narrative review.Asian spine journal · 2024Article
- Prediction of adolescent idiopathic scoliosis with machine learning algorithms using brain volumetric measurements.JOR spine · 2024Article
- Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain.Scientific reports · 2024Article
- Application of machine learning standardized integral area algorithm in measuring the scoliosis.Scientific reports · 2023Article
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Impact of Gensingen brace treatment on Lenke 5 curvatures and chronic low back pain in late adolescent and adult scoliosis patients.The South African journal of physiotherapy · 2022Article
- Brace treatment for patients with scoliosis: State of the art.The South African journal of physiotherapy · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Markerless 3D surface topography for scoliosis diagnosis and brace treatment can avoid repeated radiation known from standard X-ray analysis and possible side effects. Combined with the method of torso asymmetry analysis, curve severity and progression can be evaluated with high reliability. In the current study, a machine learning approach was utilised to classify scoliosis patients based on their trunk surface asymmetry pattern. Frontal X-ray and 3D scanning analysis with a clinical classification based on Cobb angle and spinal curve pattern were performed with 50 patients. Similar as in a previous study, each patient's trunk 3D reconstruction was used for an elastic registration of a reference surface mesh with fixed number of vertices. Subsequently, an asymmetry distance map between original and reflected torso was calculated. A fully connected neural network was then utilised to classify patients regarding their Cobb angle (mild, moderate, severe) and an Augmented Lehnert-Schroth (ALS) classification based on their full torso asymmetry distance map. The results reveal a classification success rate of 90% (SE: 80%, SP: 100%) regarding the curve severity (mild vs moderate-severe) and 50-72% regarding the ALS group. Identifying patient curve severity and treatment group was reasonably possible allowing for a decision support during diagnosis and treatment planning. Graphical abstract.
Indexed as
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33001363What OpenQuestion holds
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.