ArticleScientific reports2020
Artificial intelligence for the detection of vertebral fractures on plain spinal radiography.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 4 syntheses or guidelines pooled it, 111 citations in OpenAlex.
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled it
- Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.Radiology · 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
- Augmenting Osteoporosis Imaging with Machine Learning.Current osteoporosis reports · 2021Pooled it
- From underdiagnosis to scalable screening: Applications of artificial intelligence for vertebral compression fracture detection - a systematic review and meta-analysis of diagnostic performance.Osteoporosis and sarcopenia · 2026Review
- Vertebral fractures identified on lateral DXA images by deep learning predict incident fractures in older women.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- A machine learning-based framework for predicting hypertension using serum hematological factors.Scientific reports · 2026Article
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Spinal Cord Segmentation and Injury Detection based on Siamese Conventional WideRes Network using CT Image.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
- Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.Bioengineering (Basel, Switzerland) · 2025Review
- External Validation of a Winning Artificial Intelligence Algorithm from the RSNA 2022 Cervical Spine Fracture Detection Challenge.AJNR. American journal of neuroradiology · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- The prevention of osteoporotic vertebral fractures in eastern and in western countries.Bone reports · 2025Review
- An automated algorithm for quantitative morphometry of thoracic and lumbar vertebral bodies in lateral radiographs.JBMR plus · 2025Article
- Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning model using cross-sequence learning to identify orbital fractures in radiographs of patients under 20 Years.Frontiers in bioengineering and biotechnology · 2025Article
- Role of Artificial Intelligence in Minimizing Missed and Undiagnosed Fractures Among Trainee Residents.Journal of multidisciplinary healthcare · 2025Review
- Application and Prospects of Deep Learning Technology in Fracture Diagnosis.Current medical science · 2024Review
- Comparative efficacy of anteroposterior and lateral X-ray based deep learning in the detection of osteoporotic vertebral compression fracture.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
19 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluation of VFs. Therefore, minimizing the diagnostic errors of VFs on PTLR is crucial. Image identification based on a deep convolutional neural network (DCNN) has been recognized to be potentially effective as a diagnostic strategy; however, the accuracy for detecting VFs has not been fully investigated. A DCNN was trained with PTLR images of 300 patients (150 patients with and 150 without VFs). The accuracy, sensitivity, and specificity of diagnosis of the model were calculated and compared with those of orthopedic residents, orthopedic surgeons, and spine surgeons. The DCNN achieved accuracy, sensitivity, and specificity rates of 86.0% [95% confidence interval (CI) 82.0-90.0%], 84.7% (95% CI 78.8-90.5%), and 87.3% (95% CI 81.9-92.7%), respectively. Both the accuracy and sensitivity of the model were suggested to be noninferior to those of orthopedic surgeons. The DCNN can assist clinicians in the early identification of VFs and in managing patients, to prevent further invasive interventions and a decreased quality of life.
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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.