Introduction
Calcaneus fractures represent 60% of tarsal bone fractures and are particularly challenging to assess when they involve the posterior talocalcaneal (PTC) facet. Accurate evaluation of intra-articular fractures, including metrics such as gap area and step-off, is crucial for treatment planning but remains difficult due to limitations of using 2D cross-sections from 3D CT imaging. Deciding between surgical or conservative management from these images often leads to significant inter- and intra-observer variability, complicating clinical decisions and affecting outcomes. This study aims to develop and validate an AI-based method for automatic segmentation and 3D quantitative analysis of PTC fractures, improving assessment and providing more objective data for decision-making.
Methods
A retrospective study was conducted on CT scans from 44 patients for training using 5-fold cross-validation and 33 patients for external validation. The nnU-Net framework was trained to segment the PTC fragments and posterior talar facet (PTF). Automatic 3D measurements, including gap area, inter-articular distances, maximal step-off, and maximal gap, were computed from the segmented models. Results were validated against manual 2D measurements performed by two observers, as well as through comparison with the external validation set.
Results
The nnU-Net achieved a Dice score of 0.78 for PTC segmentation in the training set and 0.75 in the external validation set. Moderate positive correlations were observed between the 3D automatic measurements and manual 2D measurements. Specifically, the correlation between 3D gap area and 2D maximal gap measurement was Spearman’s rho = 0.62, while the correlation between 3D and 2D maximal step-off measurements was rho = 0.52. Additional correlations were found between the 3D fracture area and the 2D maximal gap and step-off measurements, with rho values of 0.65 and 0.64, respectively, indicating that the 3D analysis is consistent with corresponding 2D measurements.
Conclusion
This study introduces an AI-based method for automatic 3D analysis of calcaneus fractures, offering faster and more detailed fracture metrics, which may improve treatment planning over traditional 2D slice evaluations.