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.

Currently, morphological measurements on conventional radiographs are used to assess calcaneal fractures. The two most commonly used angles for determining the degree of depression and displacement of such a fracture are Böhler’s angle (BA) and the Critical angle of Gissane (CAG). However, conventional radiographs only provide limited amount of information and are increasingly being omitted, while computerized tomography (CT) scans are increasingly being used as they contain much more information regarding fracture configuration and displacement. It is therefore important that morphological measurements can be performed using CT-scans. The purpose of this thesis were to design a novel method to perform semi-automated morphological measurements based on 3D models of the calcaneus. The results of the novel method were compared with conventional 2D measurements of the same calcaneus.

119 lateral foot radiographs and CT-scan of the same foot were obtained. The data consisted out of three populations: 40 non-fractured, 39 fractured and 40 reconstructed calcanei. Based on the CT-scans, 3D models were reconstructed through interactive segmentation processes. Measurements on the radiographs and 3D models were performed in a specially designed Graphical User Interface. Three observers measured BA and the CAG, after which they repeated the measurements after a minimum of one week. The inter- and intra-observers reliability were calculated separately for all three populations, using the intra-class correlation coefficient (ICC). Additionally, the frequency of consensus given an allowed discrepancy was calculated.

The ICC for inter- and intra-observer reliability of the 3D measurements showed an improvement for BA and the CAG for all populations compared with the ICC of the 2D measurements. The ICC for inter- and intra-observer reliability of the 3D measured BA ranged from 0.97 to 1.00, indicating an almost perfect agreement between the observers. For the CAG, the inter- and intra-observer ICC ranged from 0.89 to 0.92 and from 0.87 to 0.96, respectively. To achieve 80% consensus for the BA among the observers, an allowable discrepancy of 20 degrees was needed for the 2D measurements, while only 5 degrees was needed for the 3D measurements. To achieve 80% of consensus for the 2D CAG measurements, an allowable discrepancy of at least 20 degrees was needed. For the fractured and reconstructed population, even an allowed discrepancy of 30 degrees was needed. For the 3D CAG measurements, 80% of consensus was achieved with an allowable discrepancy of 5 degrees, 20 degrees and 10 degrees for the non-fractured, fractured and reconstructed calcanei populations, respectively.

3D morphological measurements based on 3D models of the calcaneus showed better reliability and repeatability compared to the 2D measurements. However, the novel method of performing the measurements is not yet safe for use in clinical practice, as the measurements first should be validated. Nevertheless, the method is well suited to be employed as a reference for the development of further (semi-)automated methods to perform morphological measurements.

Background
Head-mounted augmented reality (AR) can be beneficial in orthopedic trauma surgery by projecting 3-dimensional (3D) holograms of preoperative imaging data, drilling or cutting planes, and osteosynthesis material, directly onto the surgeon’s field of view (FOV). For successful implementation of head-mounted AR in orthopedic trauma surgery, the surgeon’s experience is of utmost importance. This prospective clinical study aimed to evaluate the surgeon’s experience with head-mounted AR in orthopedic trauma surgery.
Methods
The Microsoft HoloLens 2 (Microsoft, Redmond, WA, USA) was used to project patient-specific 3D models of the patient’s fracture site as 3D holograms in the surgeon’s FOV during surgery of intra-articular fractures. The surgeon’s experience was recorded using the Simulator Sickness Questionnaire, Borg CR10 Scale, National Aeronautics and Space Administration Task Load Index, System Usability Scale, and a custom-made questionnaire.
Results
A total of 14 ORIF surgeries were performed while using a head-mounted AR device. Overall, surgeons were satisfied with AR HMDs (67.9 ± 22.3 (mean ± SD)) and wanted to use it again (70.0 ± 22.54 (mean ± SD)). Good results were reported regarding the usability of the AR HMD (65.7 ± 16.6 (mean ± SD)) and the interaction with 3D holograms 72.1 ± 15.8 (mean ± SD). However, the mental demand, size, and weight of the AR HMD were rated less favorable.
Conclusion
The surgeon’s experience with the intraoperative use of head-mounted AR in orthopedic trauma surgery is sufficient and head-mounted AR has great future potential in orthopedic trauma surgery. Future research has to focus on an ergonomic design of surgical AR HMDs, technological developments, a simple and fluent workflow, and proving clinical value of head-mounted AR.