ReviewEuropean journal of trauma and emergency surgery : official publication of the European Trauma Society2023
Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations.
Review in European journal of trauma and emergency surgery : official publication of the European Trauma Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 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
- Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Pooled it
- Review
- Review
- FAD-MIL: a weakly supervised fracture detection model based on X-ray images.Scientific reports · 2026Article
- Deep Learning Enhances Weightbearing CT Detection of Lisfranc Instability: A FIXUS-AI Ankle Insight 3D Algorithm.Cureus · 2025Article
- Review
- An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.Cureus · 2025Review
- The Emergence of Applied Artificial Intelligence in the Realm of Value Based Musculoskeletal Care.Current reviews in musculoskeletal medicine · 2025Review
- Postoperative Outcomes of Anteromedial Approach in Patients with Elbow Varus Posteromedial Rotatory Instability: A Retrospective Study.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Artificial Intelligence in Trauma and Orthopaedic Surgery: A Comprehensive Review From Diagnosis to Rehabilitation.Cureus · 2025Review
- Development of Machine Learning-based Algorithms to Predict the 2- and 5-year Risk of TKA After Tibial Plateau Fracture Treatment.Clinical orthopaedics and related research · 2025Article
- Faster, more accurate? A feasibility study on replacing human judges with artificial intelligence in video review for the Paris Olympics Taekwondo competition.Frontiers in sports and active living · 2025Article
- Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models.Current osteoporosis reports · 2024Review
- Juvenile Osteochondritis Dissecans: A Case Report.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning-Based Surgical Treatment Recommendation and Nonsurgical Prognosis Status Classification for Scaphoid Fractures by Automated X-ray Image Recognition.Biomedicines · 2024Article
- Human-Written vs AI-Generated Texts in Orthopedic Academic Literature: Comparative Qualitative Analysis.JMIR formative research · 2024Article
- A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging.Frontiers in artificial intelligence · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice.
methodsLiterature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detection of fractures on CT scans was described. Quality assessment was done with a modified version of the methodologic index for nonrandomized studies (MINORS), with a seven-item checklist. Performance of AI was defined as accuracy, F1-score and area under the curve (AUC).
resultsOf the 1140 identified studies, 17 were included. Accuracy ranged from 69 to 99%, the F1-score ranged from 0.35 to 0.94 and the AUC, ranging from 0.77 to 0.95. Based on ten studies, CNN showed a similar or improved diagnostic accuracy in addition to clinical evaluation only.
conclusionsCNNs are applicable for the detection and classification fractures on CT scans. This can improve automated and clinician-aided diagnostics. Further research should focus on the additional value of CNN used for CT scans in daily clinics.
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Registered trials
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.