ArticleKorean journal of radiology2020
Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility.
Article in Korean journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 5 of them syntheses that pooled it.
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Who cites it
56 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis.European radiology experimental · 2026Pooled 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
- Detection, Classification, and Segmentation of Rib Fractures From CT Data Using Deep Learning Models: A Review of Literature and Pooled Analysis.Journal of thoracic imaging · 2025Pooled it
- Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis.The British journal of radiology · 2024Pooled it
- Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- Evaluation of AI developed for rib fracture detection in identifying vertebral fractures on abdominal CT.BMC musculoskeletal disorders · 2026Article
- The Impact of Healthcare Professionals' Characteristics on the Evaluation of Clinical Decision Support Systems: Insights from a Cross-Country Usability and Technology Acceptance Study of the iCARE Tool.Journal of medical systems · 2026Article
- Automatic rib fracture detection on postmortem CT data using deep learning.International journal of legal medicine · 2026Article
- Computer-aided diagnosis system for thoracic computed tomography of rib fractures in older emergency patients: A preliminary study.PloS one · 2026Article
- Article
- Addressing fractures that are hard to diagnose on imaging: Radiomics or deep learning?La Radiologia medica · 2025Review
- 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
- Faster R-CNN model for target recognition and diagnosis of scapular fractures.Journal of bone oncology · 2025Article
- Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.BMC medical imaging · 2025Article
- Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm.Journal of imaging informatics in medicine · 2025Article
- Multi-stream and multi-scale fusion rib fracture segmentation network based on UXNet.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning model using cross-sequence learning to identify orbital fractures in radiographs of patients under 20 Years.Frontiers in bioengineering and biotechnology · 2025Article
- AI-assisted radiologists vs. standard double reading for rib fracture detection on CT images: A real-world clinical study.PloS one · 2025Article
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- External validation of an artificial intelligence multi-label deep learning model capable of ankle fracture classification.BMC musculoskeletal disorders · 2024Observational
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. MATERIALS AND
methodsThis study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists.
resultsA total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively,
conclusionOur CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.
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