ArticleGlobal spine journal2020
Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision.
Article in Global spine journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
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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
37 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence automated measurements of spinopelvic parameters in adult spinal deformity-a systematic review.Spine deformity · 2025Pooled it
- Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.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 · 2022Pooled it
- Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- U-Net-Based Automated Quality Control of Knee Radiographs: Dual-Center Validation and Clinical Intervention.Journal of imaging informatics in medicine · 2026Article
- Improved Chronic Low Back Pain, Radiographic Alignment, and Patient Reported Outcomes Following Postural Rehabilitation Protocols: A Case Series of Two Patients with 18- and 26-Months Follow-Up.Healthcare (Basel, Switzerland) · 2026Article
- Investigating the Effects of Image Scaling Techniques in Radiographic Measurements of Spinal Alignment and Motion: A Comparative Analysis.Journal of imaging informatics in medicine · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Association of Lumbar Sagittal Curvature Profiles with Musculoskeletal Disorders: A Pilot Radiographic Study.Diagnostics (Basel, Switzerland) · 2026Article
- Development and validation of a deep learning-powered system for multi-version global alignment and proportion score to predict mechanical complications after adult degenerative scoliosis surgery.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
- UANV: UNet-based attention network for thoracolumbar vertebral compression fracture angle measurement.Scientific reports · 2025Article
- Automated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.La Radiologia medica · 2025Article
- Development of a model for measuring sagittal plane parameters in 10-18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology.Journal of orthopaedic surgery and research · 2025Article
- A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays.Heliyon · 2024Article
- Can generative artificial intelligence pass the orthopaedic board examination?Journal of orthopaedics · 2024Article
- Deep Learning-Assisted Quantitative Measurement of Thoracolumbar Fracture Features on Lateral Radiographs.Neurospine · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- VertXNet: an ensemble method for vertebral body segmentation and identification from cervical and lumbar spinal X-rays.Scientific reports · 2024Article
- Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designCross sectional database study.
objectiveTo develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis.
methodsLateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151).
resultsThe U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant,
conclusionThis study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.
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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.