ArticleJournal of clinical medicine2019
Artificial Intelligence for Automatic Measurement of Sagittal Vertical Axis Using ResUNet Framework.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Automated diagnostic of cervical spondylosis on multimodal medical images with a multi-task deep learning model.Nature communications · 2026Article
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 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
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.Journal of neurosurgery. Spine · 2024Article
- Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Landet: an efficient physics-informed deep learning approach for automatic detection of anatomical landmarks and measurement of spinopelvic alignment.Journal of orthopaedic surgery and research · 2024Article
- Artificial Intelligence in Scoliosis: Current Applications and Future Directions.Journal of clinical medicine · 2023Review
- Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points.Bioengineering (Basel, Switzerland) · 2023Article
- Emerging Technologies within Spine Surgery.Life (Basel, Switzerland) · 2023Review
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
- Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs.Medicina (Kaunas, Lithuania) · 2022Article
- Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network.Journal of digital imaging · 2022Article
- An artificial neural network model based on standing lateral radiographs for predicting sitting pelvic tilt in healthy adults.Frontiers in surgery · 2022Article
- The application of artificial intelligence in spine surgery.Frontiers in surgery · 2022Review
- A fresh look at spinal alignment and deformities: Automated analysis of a large database of 9832 biplanar radiographs.Frontiers in bioengineering and biotechnology · 2022Article
- Article
- Comparison of 3D and 2D characterization of spinal geometry from biplanar X-rays: a large cohort study.Quantitative imaging in medicine and surgery · 2021Article
- Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs.Scientific reports · 2021Article
- A Review on the Use of Artificial Intelligence in Spinal Diseases.Asian spine journal · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
We present an automated method for measuring the sagittal vertical axis (SVA) from lateral radiography of whole spine using a convolutional neural network for keypoint detection (ResUNet) with our improved localization method. The algorithm is robust to various clinical conditions, such as degenerative changes or deformities. The ResUNet was trained and evaluated on 990 standing lateral radiographs taken at Chang Gung Memorial Hospital, Linkou and performs SVA measurement with median absolute error of 1.183 ± 0.166 mm. The 5-mm detection rate of the C7 body and the sacrum are 91% and 87%, respectively. The SVA calculation takes approximately 0.2 s per image. The intra-class correlation coefficient of the SVA estimates between the algorithm and physicians of different years of experience ranges from 0.946 to 0.993, indicating an excellent consistency. The superior performance of the proposed method and its high consistency with physicians proved its usefulness for automatic measurement of SVA in clinical settings.
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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.