ArticleEmergency radiology2022
Detection of acute rib fractures on CT images with convolutional neural networks: effect of location and type of fracture and reader's experience.
Article in Emergency radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Automatic rib fracture detection on postmortem CT data using deep learning.International journal of legal medicine · 2026Article
- 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
- A deep learning-based approach to automated rib fracture detection and CWIS classification.International journal of computer assisted radiology and surgery · 2025Article
- Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.BMC medical imaging · 2025Article
- The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.Japanese journal of radiology · 2024Review
- Oblique Axis Rib Stretch and Curved Planar Reformats in Patients for Rib Fracture Detection and Characterization: Feasibility and Clinical Application.Emergency medicine international · 2023Article
- Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness.Scientific reports · 2022Article
- Detection of Costal Cartilage Fractures on CT Images With Computer-aided Detection System for Rib Fractures.In vivo (Athens, Greece)Article
Corrections and comments
- Erratum issued
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe evaluation of all ribs on thin-slice CT images is time consuming and it can be difficult to accurately assess the location and type of rib fracture in an emergency. The aim of our study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of acute rib fractures on thoracic CT images and to investigate the effect of the CNN algorithm on radiologists' performance.
methodsThe dataset for development of a CNN consisted of 539 thoracic CT scans with 4906 acute rib fractures. A three-dimensional faster region-based CNN was trained and evaluated by using tenfold cross-validation. For an observer performance study to investigate the effect of CNN outputs on radiologists' performance, 30 thoracic CT scans (28 scans with 90 acute rib fractures and 2 without rib fractures) which were not included in the development dataset were used. Observer performance study involved eight radiologists who evaluated CT images first without and second with CNN outputs. The diagnostic performance was assessed by using figure of merit (FOM) values obtained from the jackknife free-response receiver operating characteristic (JAFROC) analysis.
resultsWhen radiologists used the CNN output for detection of rib fractures, the mean FOM value significantly increased for all readers (0.759 to 0.819, P = 0.0004) and for displaced (0.925 to 0.995, P = 0.0028) and non-displaced fractures (0.678 to 0.732, P = 0.0116). At all rib levels except for the 1st and 12th ribs, the radiologists' true-positive fraction of the detection became significantly increased by using the CNN outputs.
conclusionThe CNN specialized for the detection of acute rib fractures on CT images can improve the radiologists' diagnostic performance regardless of the type of fractures and reader's experience. Further studies are needed to clarify the usefulness of the CNN for the detection of acute rib fractures on CT images in actual clinical practice.
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