ArticleThe British journal of radiology2023
An automatic fresh rib fracture detection and positioning system using deep learning.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- 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
- Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis.The British journal of radiology · 2024Pooled it
- Scintigraphy for the diagnosis of primary unrecognised fractures in multiple trauma patients - a prospective, blinded, monocentric study.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Trial
- Integration of detection and tracking networks for automated rib multiplanar reconstruction: a feasibility study for fracture diagnosis.European radiology experimental · 2026Article
- Automatic rib fracture detection on postmortem CT data using deep learning.International journal of legal medicine · 2026Article
- A deep learning-based approach to automated rib fracture detection and CWIS classification.International journal of computer assisted radiology and surgery · 2025Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- 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
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the performance and robustness of a deep learning-based automatic fresh rib fracture detection and positioning system (FRF-DPS).
methodsCT scans of 18,172 participants admitted to eight hospitals from June 2009 to March 2019 were retrospectively collected. Patients were divided into development set (14,241), multicenter internal test set (1612), and external test set (2319). In internal test set, sensitivity, false positives (FPs) and specificity were used to assess fresh rib fracture detection performance at the lesion- and examination-levels. In external test set, the performance of detecting fresh rib fractures by radiologist and FRF-DPS were evaluated at lesion, rib, and examination levels. Additionally, the accuracy of FRF-DPS in rib positioning was investigated by the ground-truth labeling.
resultsIn multicenter internal test set, FRF-DPS showed excellent performance at the lesion- (sensitivity: 0.933 [95%CI, 0.916-0.949], FPs: 0.50 [95%CI, 0.397-0.583]) and examination-level. In external test set, the sensitivity and FPs at the lesion-level of FRF-DPS (0.909 [95%CI, 0.883-0.926],
conclusionFRF-DPS achieved high detection rate of fresh rib fractures with low FP values, and precise positioning of ribs, thus can be used in clinical practice to improve the detection rate and work efficiency. ADVANCES IN KNOWLEDGE: We developed the FRF-DPS system which can detect fresh rib fractures and rib position, and evaluated by a large amount of multicenter data.
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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.