A deep learning-based approach to automated rib fracture detection and CWIS classification Article Swipe
Victoria Marting
,
Noor Borren
,
Max R Van Diepen
,
Esther M.M. Van Lieshout
,
Mathieu M. E. Wijffels
,
Theo van Walsum
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11548-025-03390-5
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s11548-025-03390-5
Purpose Trauma-induced rib fractures are a common injury. The number and characteristics of these fractures influence whether a patient is treated nonoperatively or surgically. Rib fractures are typically diagnosed using CT scans, yet 19.2–26.8% of fractures are still missed during assessment. Another challenge in managing rib fractures is the interobserver variability in their classification. Purpose of this study was to develop and assess an automated method that detects rib fractures in CT scans, and classifies them according to the Chest Wall Injury Society (CWIS) classification. Methods 198 CT scans were collected, of which 170 were used for training and internal validation, and 28 for external validation. Fractures and their classifications were manually annotated in each of the scans. A detection and classification network was trained for each of the three components of the CWIS classifications. In addition, a rib number labeling network was trained for obtaining the rib number of a fracture. Experiments were performed to assess the method performance. Results On the internal test set, the method achieved a detection sensitivity of 80%, at a precision of 87%, and an F1-score of 83%, with a mean number of FPPS (false positives per scan) of 1.11. Classification sensitivity varied, with the lowest being 25% for complex fractures and the highest being 97% for posterior fractures. The correct rib number was assigned to 94% of the detected fractures. The custom-trained nnU-Net correctly labeled 95.5% of all ribs and 98.4% of fractured ribs in 30 patients. The detection and classification performance on the external validation dataset was slightly better, with a fracture detection sensitivity of 84%, precision of 85%, F1-score of 84%, FPPS of 0.96 and 95% of the fractures were assigned the correct rib number. Conclusion The method developed is able to accurately detect and classify rib fractures in CT scans, there is room for improvement in the (rare and) underrepresented classes in the training set.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11548-025-03390-5
- https://link.springer.com/content/pdf/10.1007/s11548-025-03390-5.pdf
- OA Status
- hybrid
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410445179
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410445179Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11548-025-03390-5Digital Object Identifier
- Title
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A deep learning-based approach to automated rib fracture detection and CWIS classificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-16Full publication date if available
- Authors
-
Victoria Marting, Noor Borren, Max R Van Diepen, Esther M.M. Van Lieshout, Mathieu M. E. Wijffels, Theo van WalsumList of authors in order
- Landing page
-
https://doi.org/10.1007/s11548-025-03390-5Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s11548-025-03390-5.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11548-025-03390-5.pdfDirect OA link when available
- Concepts
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Rib cage, False positive paradox, Medicine, Radiology, Fracture (geology), Artificial intelligence, Nuclear medicine, Computer science, Geology, Anatomy, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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