ArticleThe British journal of radiology2022
Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Artificial Intelligence for Detection of Rib Fracture on X-ray and Computed Tomography Imaging: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Artificial intelligence in commercial fracture detection products: a systematic review and meta-analysis of diagnostic test accuracy.Scientific reports · 2024Pooled it
- Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2023Pooled it
- Pneumothorax and Hemothorax: Practical Approaches for Radiologists.Journal of the Korean Society of Radiology · 2026Review
- Deep learning in rib fracture imaging: study quality assessment using the Must AI Criteria-10 (MAIC-10) checklist for artificial intelligence in medical imaging.Insights into imaging · 2025Article
- Multidisciplinary Evaluation of an AI-Based Pneumothorax Detection Model: Clinical Comparison with Physicians in Edge and Cloud Environments.Journal of multidisciplinary healthcare · 2025Article
- Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm.Archives of academic emergency medicine · 2025Article
- Commercially-available AI algorithm improves radiologists' sensitivity for wrist and hand fracture detection on X-ray, compared to a CT-based ground truth.European radiology · 2024Article
- AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.Radiology. Artificial intelligence · 2024Article
- Commercially available artificial intelligence tools for fracture detection: the evidence.BJR open · 2024Review
- Eyes on the prize: harnessing computer vision for automated detection of traumatic rib and clavicle fractures in chest radiographs.Trauma surgery & acute care open · 2024Article
- The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- The American Society of Emergency Radiology (ASER) AI/ML expert panel: inception, mandate, work products, and goals.Emergency radiology · 2023Article
- Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.Emergency radiology · 2023Article
- A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.Emergency radiology · 2023Article
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTrauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network - Annalise CXR V1.2 (Annalise.ai) - for detection of traumatic injuries on supine chest radiographs.
methodsChest radiographs with a CT performed within 24 h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen's κ and sensitivity/specificity for both AI and radiologists were calculated.
resultsThere were 1404 cases identified with a median age of 52 (IQR 33-69) years, 949 males. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (
conclusionThe evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes. ADVANCES IN KNOWLEDGE: Clinically useful AI programs represent promising decision support tools.
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