ArticleBMC medical imaging2025
Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Evaluating the age of rib fractures throughInternational journal of legal medicine · 2026Article
- Enhanced YOLOv8 with Hybrid Attention Gating Block for Intertrochanteric Fracture Detection in Complex X-Ray Images.Journal of imaging informatics in medicine · 2026Article
- Deep learning for radiographic differentiation between lateral malleolar avulsion fractures and subfibular ossicles.iScience · 2026Article
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study.JMIR medical informatics · 2025Article
- Comparative Analysis of Conventional and Focused Data Augmentation Methods in Rib Fracture Detection in CT Images.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundAccurate detection and grading of fresh rib fractures are crucial for patient management but remain challenging due to the complexity of rib structures on CT images.
methodsChest CT images from 383 patients with rib fractures were retrospectively analyzed. The dataset was divided into a training set (n = 306) and an internal testing set (n = 77). An external testing set of 50 patients from the public RibFrac dataset was included. Fractures were classified into severe and non-severe categories. A modified YOLO-based deep learning model was developed for detection and grading. Performance was compared with thoracic surgeons using precision, recall, mAP50, and F1 score.
resultsThe deep learning model showed excellent performance in diagnosing fresh rib fractures. For all fractures types in internal test set, the precision, recall, mAP50, and F1 score were 0.963, 0.934, 0.972, and 0.948, respectively. The model outperformed thoracic surgeons of varying experience levels (all p < 0.01).
conclusionThe proposed deep learning model can automatically detect and grade fresh rib fractures with accuracy comparable to that of physicians. This model helps improve diagnostic accuracy, reduce physician workload, save medical resources, and strengthen health care in resource-limited areas. CLINICAL TRIAL NUMBER: Not applicable.
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