ArticleCommunications biology2019
Development and validation of deep learning algorithms for scoliosis screening using back images.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 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
55 citing papers in PubMed, 140 citations in OpenAlex.
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.Journal of clinical medicine · 2026Review
- Millimeter-Wave Imaging for Idiopathic Scoliosis Screening: Diagnostic Accuracy Study.JMIR pediatrics and parenting · 2026Article
- EDRNet-based adolescent idiopathic scoliosis screening method using bare-back images.BMC medical informatics and decision making · 2026Article
- Advanced machine learning model for Cobb angle progression in adolescent idiopathic scoliosis with surface topography: a multicenter, prospective, observational study.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 radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.Spine deformity · 2026Article
- Enhanced Vision Transformer with Custom Attention Mechanism for Automated Idiopathic Scoliosis Classification.Journal of imaging informatics in medicine · 2026Article
- Prevalence and factors associated with adolescent idiopathic scoliosis in Shanghai, China: an artificial intelligence-assisted screening approach.Frontiers in public health · 2026Article
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN.Diagnostics (Basel, Switzerland) · 2025Article
- Three-dimensional markerless surface topography approach with convolutional neural networks for adolescent idiopathic scoliosis screening.Scientific reports · 2025Article
- Heatmap-Based Active Shape Model for Landmark Detection in Lumbar X-ray Images.Journal of imaging informatics in medicine · 2025Article
- Intelligent Evaluation Method for Scoliosis at Home Using Back Photos Captured by Mobile Phones.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence.Spine deformity · 2024Review
- Conditional generative adversarial network-assisted system for radiation-free evaluation of scoliosis using a single smartphone photograph: a model development and validation study.EClinicalMedicine · 2024Article
- Explainable Deep-Learning-Based Gait Analysis of Hip-Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression.Sensors (Basel, Switzerland) · 2024Article
- Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.Journal of neurosurgery. Spine · 2024Article
- Deep Learning-Based Prediction Model for the Cobb Angle in Adolescent Idiopathic Scoliosis Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images.Heliyon · 2024Article
- The Application of Surface Electromyography Technology in Evaluating Paraspinal Muscle Function.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
22 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5-5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposure due to their low positive predictive values. The application of deep learning algorithms has the potential to reduce unnecessary referrals and costs in scoliosis screening. Here, we developed and validated deep learning algorithms for automated scoliosis screening using unclothed back images. The accuracies of the algorithms were superior to those of human specialists in detecting scoliosis, detecting cases with a curve ≥20°, and severity grading for both binary classifications and the four-class classification. Our approach can be potentially applied in routine scoliosis screening and periodic follow-ups of pretreatment cases without radiation exposure.
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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.