ArticleSkeletal radiology2024
An overview of the performance of AI in fracture detection in lumbar and thoracic spine radiographs on a per vertebra basis.
Article in Skeletal radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- 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
- AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment.Diagnostics (Basel, Switzerland) · 2026Review
- Bone-CNN: A Lightweight Deep Learning Architecture for Multi-Class Classification of Primary Bone Tumours in Radiographs.Biomedicines · 2026Article
- Advancing Spine Fracture Detection: The Role of Artificial Intelligence in Clinical Practice.Korean journal of neurotrauma · 2025Review
- Thermal profile of the spine for the assessment of bone metabolic disorders.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSubtle spinal compression fractures can easily be missed. AI may help in interpreting these images. We propose to test the performance of an FDA-approved algorithm for fracture detection in radiographs on a per vertebra basis, assessing performance based on grade of compression, presence of foreign material, severity of degenerative changes, and acuity of the fracture.
methodsThoracic and lumbar spine radiographs with inquiries for fracture were retrospectively collected and analyzed by the AI. The presence or absence of fracture was defined by the written report or cross-sectional imaging where available. Fractures were classified semi-quantitatively by the Genant classification, by acuity, by the presence of foreign material, and overall degree of degenerative change of the spine. The results of the AI were compared to the gold standard.
resultsA total of 512 exams were included, depicting 4114 vertebra with 495 fractures. Overall sensitivity was 63.2% for the lumbar spine, significantly higher than the thoracic spine with 50.6%. Specificity was 96.7 and 98.3% respectively. Sensitivity increased with fracture grade, without a significant difference between grade 2 and 3 compression fractures (lumbar spine: grade 1, 52.5%; grade 2, 72.3%; grade 3, 75.8%; thoracic spine: grade 1, 42.4%; grade 2, 60.0%; grade 3, 60.0%). The presence of foreign material and a high degree of degenerative changes reduced sensitivity.
conclusionOverall performance of the AI on a per vertebra basis was degraded in clinically relevant scenarios such as for low-grade compression fractures.
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