Introduction: Trauma-induced rib fractures are a common injury, affecting millions of individuals globally each year. The number and characteristics of these fractures influence whether a patient is treated conservatively or surgically. Rib fractures are typically diagnosed using CT scans, yet 19.2% to 26.8% of fractures are still missed during assessment. Another challenge in managing rib fractures is the interobserver variability in their classification. In 2023, a deep learning-based algorithm for the automatic detection and classification of rib fractures (DCRibFrac v1.0) was developed based on the Chest Wall Injury Society (CWIS) taxonomy. Although DCRibFrac v1.0 demonstrated promising results, there remained room for improvement, particularly in the accuracy of rib number labelling. This project aims to develop and assess DCRibFrac v2.0, an enhanced version of the original algorithm.
Methods: Two novel approaches for automatic rib number labelling were proposed and evaluated: a pre-trained deep learning model named TotalSegmentator and a custom-developed nnUNet. Additionally, three nnDetection models were developed based on multi-centre data for the automatic detection of fractures and the classification of their type, displacement, and location. The performance of DCRibFrac v2.0 was evaluated. Finally, an external validation was conducted to assess the generalizability and robustness of DCRibFrac v2.0.
Results: For the development and evaluation of DCRibFrac v2.0 a total of 170 patients were included. The custom-developed nnUNet was the best-performing method for rib number labelling, correctly labelling 95.5% of all ribs and 98.4% of fractured ribs in 30 patients. On the internal test set, DCRibFrac v2.0 achieved a detection sensitivity of 80%, a precision of 87%, and an F1 score of 83%, with a mean FPPS (false positives per scan) rate of 1.11. Classification sensitivity varied across fracture types, 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 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 assigned the correct rib number.
Conclusion: The developments resulting in DCRibFrac v2.0 have improved the automatic detection and classification of rib fractures, with particular advancements in rib number labelling performance and detection precision. These improvements are important steps towards establishing a more accurate and standardised method for rib fracture assessment, which could enhance clinical decision-making and improve patient outcomes in trauma care.