ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2024
External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans.
Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.Emergency radiology · 2026Pooled it
- Artificial intelligence in risk prediction and diagnosis of vertebral fractures.Scientific reports · 2024Pooled 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
- Deep learning model for simultaneous vertebral level localization and fracture detection on DXA vertebral fracture assessment images.Osteoporosis and sarcopenia · 2026Article
- Recognition and reporting of vertebral fractures in thoracic and abdominal computed tomography images - have we learnt our lesson?Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Artificial Intelligence-Assisted Structural Analysis of Bones with Paget's Disease of Bone and Osteoporosis: Lessons from Mouse Models.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Bone Fracture Detection: A Review of Evidence, Limitations, and Clinical Integration.Cureus · 2025Review
- Development and multi-center validation of a software program, Ofeye 3.0, for automated all-inclusive vertebral fracture detection with chest/abdominal CT images.Journal of orthopaedic translation · 2025Article
- Transferable CNN-based data mining approaches for medical imaging: application to spine DXA scans for osteoporosis detection.Frontiers in computational neuroscience · 2025Article
- Review
- Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.Journal of clinical medicine · 2024Review
- Perspectives on Fracture Liaison Service in Austria: clinical and economic considerations.Frontiers in endocrinology · 2024Review
Corrections and comments
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9 authors at 4 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest. PURPOSE: To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort.
methodsTwo Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for prevalent VFs in a process blinded to clinical information. The performance of the CNN model was evaluated against reference standard readings by the area under the receiver operating characteristics curve (AUROC), accuracy, Cohen's kappa, sensitivity, and specificity.
resultsA total of 4,810 subjects were included, with a median age of 62 years (IQR 56-67), of which 2,654 (55.2%) were females. The scans were acquired between January 2013 and January 2019 on 16 different CT scanners from three different manufacturers. 2,773 (57.7%) were abdominal CTs. A total of 628 scans (13.1%) had ≥1 VF (grade 2-3), representing 899 fractured vertebrae out of a total of 48,584 (1.9%) visualized vertebral bodies. The CNN's performance in identifying scans with ≥1 moderate or severe fractures achieved an AUROC of 0.94 (95% CI: 0.93-0.95), accuracy of 93% (95% CI: 93%-94%), kappa of 0.75 (95% CI: 0.72-0.77), a sensitivity of 94% (95% CI: 92-96%) and a specificity of 93% (95% CI: 93-94%).
conclusionThe algorithm demonstrated excellent performance in the identification of vertebral fractures in a cohort of chest and abdominal CT scans of Chinese patients ≥50 years.
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