In this study, we aimed to clarify proximal femur and acetabular structural risk factors associated with low-energy acetabular fractures in the elderly using three-dimensional (3D) computed tomography (CT). Pelvic bones and femurs were segmented and modeled in 3D from abdominopelvic CT images of 121 acetabular fracture patients (mean age 72±12 years, range 50-98 years, 31 females and 90 males) and 121 age-gender matched controls with no fracture. A set of geometric parameters of the proximal femur and the acetabulum was measured. An independentsamples t-test or a Mann-Whitney U-test was used for statistical analyses. The fractured side was used for proximal femur geometry, while the contralateral side was used for acetabular geometry. The neck shaft angle (NSA) was significantly smaller (mean 122.1° [95% CI 121.1°-123.2°] vs. 124.6° [123.6°-125.6°], p = 0.001) and the femoral neck axis length (FNALb) was significantly longer (78.1 mm [77.0-79.2 mm] vs. 76.0 mm [74.8-77.2 mm], p = 0.026) in the fracture group than in the controls when genders were combined. The NSA was significantly smaller both for females (120.2° [117.8°-122.6°] vs. 124.7° [122.5°-127.0°], p = 0.007) and for males (122.7° [121.5°-123.8°] vs. 124.6° [123.4°-125.7°], p = 0.006) in the fracture group. However, only males showed a significantly longer FNALb (80.0 mm [78.9-81.1 mm] vs. 77.8 mm [76.6-79.0 mm], p = 0.025). No statistically significant associations of acetabular geometry with fractures were found. However, the mean values of the acetabular angle of Sharp (34°), the lateral center-edge angle (40°), the anterior center-edge angle (62°), and the posterior center-edge angle (105°) indicated possible over-coverage. In conclusion, our findings suggest that proximal femur geometry is associated with low-energy acetabular fractures. Especially elderly subjects with an NSA smaller than normal have an increased risk of acetabular fractures.

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Associate Editor Joel D. Stitzel oversaw the review of this article.

We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. Introduction In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). Methods The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. Results Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75-0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67-0.97]. Conclusion CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.