Although the number of patients with osteoporosis is increasing worldwide, diagnosis and treatment are presently inadequate. In this study, we developed a deep learning model to predict bone mineral density (BMD) and T-score from chest X-rays, which are one of the most common, easily accessible, and low-cost medical imaging examination methods. The dataset used in this study contained patients who underwent dual-energy X-ray absorptiometry (DXA) and chest radiography at six hospitals between 2010 and 2021. We trained the deep learning model through ensemble learning of chest X-rays, age, and sex to predict BMD using regression and T-score for multiclass classification. We assessed the following two metrics to evaluate the performance of the deep learning model: (1) correlation between the predicted and true BMDs and (2) consistency in the T-score between the predicted class and true class. The correlation coefficients for BMD prediction were hip = 0.75 and lumbar spine = 0.63. The areas under the curves for the T-score predictions of normal, osteopenia, and osteoporosis diagnoses were 0.89, 0.70, and 0.84, respectively. These results suggest that the proposed deep learning model may be suitable for screening patients with osteoporosis by predicting BMD and T-score from chest X-rays.

Background: The fixation strength of bone screws depends on bone mineral density (BMD), so it is important to evaluate bone strength at fracture sites. Few studies have investigated BMD in the pelvis. The aims of this study were to measure the regional Hounsfield unit (HU) values in the cancellous bone of the acetabulum and pelvic ring and to compare these values between young and older patients.Methods: This study enrolled young patients with high-energy trauma (aged 20–44 years; young group) and older patients with low-energy trauma (aged 65–89 years; older group). Patients without pelvic computed tomography (CT) scans, those with pelvic bone implants, and those who died were excluded. The HU values on the contralateral (non-fractured) side of the pelvis were measured on CT scans. The CT data were divided into 7 areas: the pubic bone, the anterior and posterior walls and roof of the acetabulum, the ischial tuberosity, the body of the ilium, and the third lumbar vertebra. The HU values in each area were compared between the young and older groups.Results: Sixty-one young patients and 154 older patients were included in the study. The highest HU value was in the roof of the acetabulum regardless of age and sex. HU values were significantly higher in the ischial tuberosity and body of the ilium and lower in the pubic bone and anterior wall. The HU values in all pelvic areas were significantly lower in the older group than in the young group, especially in the anterior area.Conclusions: HU values in the 6 pelvic areas were not uniform and were strongly related to load distribution. The HU distribution and age-related differences could explain the characteristic causes and patterns of acetabular fractures in the older and may help in surgical treatment.

Background: Sacral fractures are often difficult to diagnose on radiographs. Computed tomography (CT) and magnetic resonance imaging (MRI) can improve the detection rate but cannot always be performed. The accuracy of artificial intelligence (AI) in detecting orthopaedic fractures is now comparable with that of orthopaedic specialists. However, the ability of AI to detect sacral fractures has not been investigated, to our knowledge. We hypothesized that the ability to detect sacral fractures on radiographs could be improved by using AI, and aimed to develop an AI model to detect sacral fractures accurately on radiographs with better accuracy than that of orthopaedic surgeons. Methods: Subjects were patients with suspected pelvic fractures for whom radiographs and CT scans had been obtained. The radiographs were labeled according to sacral fracture status based on CT results. The data set was divided into a training set (2,038 images) and a test set (200 images). Eight convolutional neural network (CNN) models were trained using the training set. Post-trained models were used to evaluate their discrimination ability. The detection ability of 4 experienced orthopaedic surgeons was also measured using the same test set. The results of fracture assessment by the orthopaedic surgeons were compared with those of the 3 CNNs with the greatest area under the receiver operating characteristic curve. Results: Among the 8 trained models, the highest areas under the curve were for InceptionV3 (0.989), Xception (0.987), and Inception ResNetV2 (0.984). The detection rate was significantly higher for these 3 CNNs than for the orthopaedic surgeons. Conclusions: By enhancing the processing of probabilistic tasks and the communication of their results, AI may be better able to detect sacral fractures than orthopaedic surgeons. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Case: Acute compartment syndrome (ACS) with acquired hemophilia A (AHA) is rare and has no established treatment strategy. A 64-year-old woman presented with a giant hematoma in the rectus abdominis. Laboratory findings included decreased hemoglobin and increased activated partial thromboplastin time. Arterial embolization was performed for hemostasis. After catheter removal, she developed severe arm pain and numbness with blistering. Fasciotomy was performed to decrease intracompartmental pressure. Laboratory investigations revealed decreased factor VIII (FVIII) activity and increased FVIII inhibitor. AHA was diagnosed and treated with immunosuppressive and FVIII inhibitor-bypassing agents. Conclusions: Fasciotomy should be performed promptly if ACS with AHA is suspected.

This review is based on the literature published between 2000 and 2023 and presents an up-to-date evidence-based discussion of anatomical considerations relevant to the management of pelvic trauma. In addition, it discusses the significance of the pelvic ligaments in stabilizing the pelvic ring and of bone mineral density (BMD) in fragility fracture of the pelvis (FFP), which is becoming increasingly common in today’s aging societies. Following an overview of the anatomy and function of the sacroiliac joint (SIJ), the pubic symphysis, and the surrounding ligaments, the suitability of the widely-used Young-Burgess classification (YB) of high-energy impact pelvic ring fractures, which emphasizes the role of the ligaments in pelvic ring injuries, is discussed. Based on the current body of knowledge, using 2.5 cm of pubic symphysis diastasis as the determinant for surgical intervention in anterior-posterior compression fracture is questioned, and evaluation under anesthesia and lateral stress radiography for accurate diagnosis and treatment planning is proposed instead. The review underscores the need for further research on how the pelvic ligaments can provide optimal stability in the treatment of various types of pelvic fracture. On the other hand, for fragility fracture of pelvis (FFP) in older adults caused by low-energy trauma, the emphasis is on the fragility of the bones. To better manage FFP, the importance of understanding the distribution of BMD in the pelvis is highlighted. Dual-energy X-ray absorptiometry is a common method for measuring BMD, but it has drawbacks. The advantages of measuring BMD using Hounsfield units on computed tomography scans as an alternative method are discussed. An understanding of these issues may lead to better management of the increasing number of FFP cases in older people with reduced BMD.