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.

Introduction: Trauma-induced rib fractures are a common injury, affecting millions of individuals globally each year. Although anteroposterior thoracic radiographs are part of the standard posttraumatic screening, the most sensitive modality, and therefore golden standard for diagnosing rib fractures, is computed tomography (CT). Still, between 19.2% and 26.8% of rib fractures are missed. Another problem encountered in rib fracture treatment management is the large interobserver variability on their taxonomy. This thesis aims to automate rib fracture detection and improve consistency in their classification by developing a Deep Learning (DL) model, using CT data.

Methods: The rib fractures were classified according to the Chest Wall Injury Society (CWIS) taxonomy, evaluating rib fracture’s type, displacement and location. Furthermore, the ribs were numbered from 1 up to and including 12 from cranio-caudal direction. For the detection and three CWIS labels, three classification models of the nnDetection framework were trained. The rib numbering consisted of a trained nnU-Net segmentation model. The four models were combined to obtain the proposed DCRibFrac model.

Experiments and results: The dataset is composed of retrospectively collected and anonymized CT scans of 100 randomly selected patients (1010 rib fractures) who were admitted to the Erasmus MC following blunt chest trauma. On the internal test set, DCRibFrac achieved a detection sensitivity of 77%, precision of 79%, and F1-score of 78%, with a mean false-positives per scan of 2.26. The type labels had the lowest scores, with sensitivities between 17% and 90%. The displacement labels had sensitivities between 43% and 91%. The location labels had the highest scores, with sensitivities between 88% and 96%. The rib number was correct in 72% of the rib fractures when wrong segmentations were excluded.

Conclusion: The proposed DL model automates acute rib fracture detection and reaches a sensitivity that is on par with clinicians. This model is the first, to the authors’ knowledge, to incorporate the CWIS taxonomy and shows its potential for achieving a consistent classification.