A prospective study compared three commercial AI tools for fracture detection on x-ray, showing moderate-to-high accuracy for simple cases but weaker performance in complex scenarios.
Key Details
- 1Three AI models (Rayvolve, BoneView, and RBFracture) assessed on x-rays from 1,037 adult patients across 22 anatomical regions.
- 2Fractures present in 29.6% of cases; 13.7% had acute fractures; 6.7% had multiple fractures.
- 3Overall AUCs: Rayvolve 84.9%, BoneView 84%, RBFracture 77.2%.
- 4Rayvolve showed highest sensitivity (79.5%), BoneView balanced performance, RBFracture highest specificity (93.6%).
- 5Performance dropped for multiple fractures (AUCs 64.2%-73.4%) and in dislocations.
- 6Researchers recommend these AI tools as adjuncts rather than replacements for clinicians.
Why It Matters
This head-to-head analysis provides critical insight into the current strengths and limitations of commercially available AI fracture detection tools, highlighting the need for further real-world validation and underscoring that radiologists remain essential in complex cases.
Source
AuntMinnie
Related News
•AuntMinnie
AI Boosts Agreement in CAD-RADS Classification on Cardiac CT
Deep learning AI improves interreader agreement in CAD-RADS assessments on coronary CT angiography.
•Radiology Business
AI Automates Head CT Reformatting, Improving Efficiency and Consistency
Researchers at UC Irvine used deep learning to automate head CT reformatting, improving workflow standardization and efficiency.
•AuntMinnie
Comparing False-Positive Findings: AI vs. Radiologists in DBT Screening
AI and radiologists differ in the types and patient characteristics of false-positive findings in digital breast tomosynthesis breast cancer screening.