SUMMARY: Although neurofibromatosis type 1 (NF-1) is commonly considered neurocutaneous, severe arterial and venous abnormalities have been noted. Our patient, a 28-year-old woman, had bilateral giant extracranial aneurysms of the internal carotid arteries as well as skull base meningoceles involving the jugular foramina and aberrant jugular veins. CT and MR imaging, as well as digital subtraction and/or other angiography techniques, may be required to clarify pathology in patients with suggested vascular lesions.

Background: Splenic injury is the most common solid visceral injury in blunt abdominal trauma, and high-resolution abdominal computed tomography (CT) can adequately detect the injury. However, these lethal injuries sometimes have been overlooked in current practice. Deep learning (DL) algorithms have proven their capabilities in detecting abnormal findings in medical images. The aim of this study is to develop a three-dimensional, weakly supervised DL algorithm for detecting splenic injury on abdominal CT using a sequential localization and classification approach. Material and methods: The dataset was collected in a tertiary trauma center on 600 patients who underwent abdominal CT between 2008 and 2018, half of whom had splenic injuries. The images were split into development and test datasets at a 4 : 1 ratio. A two-step DL algorithm, including localization and classification models, was constructed to identify the splenic injury. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Grad-CAM (Gradient-weighted Class Activation Mapping) heatmaps from the test set were visually assessed. To validate the algorithm, we also collected images from another hospital to serve as external validation data. Results: A total of 480 patients, 50% of whom had spleen injuries, were included in the development dataset, and the rest were included in the test dataset. All patients underwent contrast-enhanced abdominal CT in the emergency room. The automatic two-step EfficientNet model detected splenic injury with an AUROC of 0.901 (95% CI: 0.836–0.953). At the maximum Youden index, the accuracy, sensitivity, specificity, PPV, and NPV were 0.88, 0.81, 0.92, 0.91, and 0.83, respectively. The heatmap identified 96.3% of splenic injury sites in true positive cases. The algorithm achieved a sensitivity of 0.92 for detecting trauma in the external validation cohort, with an acceptable accuracy of 0.80. Conclusions: The DL model can identify splenic injury on CT, and further application in trauma scenarios is possible.

Objective: To identify the feasibility and efficiency of deep convolutional neural networks (DCNNs) in the detection of ankle fractures and to explore ensemble strategies that applied multiple projections of radiographs. Ankle radiographs (AXRs) are the primary tool used to diagnose ankle fractures. Applying DCNN algorithms on AXRs can potentially improve the diagnostic accuracy and efficiency of detecting ankle fractures. Methods: A DCNN was trained using a trauma image registry, including 3102 AXRs. We separately trained the DCNN on anteroposterior (AP) and lateral (Lat) AXRs. Different ensemble methods, such as “sum-up,” “severance-OR,” and “severance-Both,” were evaluated to incorporate the results of the model using different projections of view. Results: The AP/Lat model’s individual sensitivity, specificity, positive-predictive value, accuracy, and F1 score were 79%/84%, 90%/86%, 88%/86%, 83%/85%, and 0.816/0.850, respectively. Furthermore, the area under the receiver operating characteristic curve (AUROC) of the AP/Lat model was 0.890/0.894 (95% CI: 0.826–0.954/0.831–0.953). The sum-up method generated balanced results by applying both models and obtained an AUROC of 0.917 (95% CI: 0.863–0.972) with 87% accuracy. The severance-OR method resulted in a better sensitivity of 90%, and the severance-Both method obtained a high specificity of 94%. Conclusion: Ankle fracture in the AXR could be identified by the trained DCNN algorithm. The selection of ensemble methods can depend on the clinical situation which might help clinicians detect ankle fractures efficiently without interrupting the current clinical pathway. Advances in knowledge: This study demonstrated different ensemble strategies of AI algorithms on multiple view AXRs to optimize the performance in various clinical needs.

Background: Traumatic hollow viscus injury (THVI) is one of the most difficult challenges in the trauma setting. Computed tomography (CT) is the most common modality used to diagnose THVI; however, various performance outcomes of CT have been reported. We conducted a systematic review and meta-analysis to analyze how precise and reliable CT is as a tool for the assessment of THVI. Method: A systematic review and meta-analysis were conducted on studies on the use of CT to diagnose THVI. Publications were retrieved by performing structured searches in databases, review articles and major textbooks. For the statistical analysis, summary receiver operating characteristic (SROC) curves were constructed using hierarchical models. Results: Sixteen studies enrolling 12,514 patients were eligible for the final analysis. The summary sensitivity and specificity of CT for the diagnosis of THVI were 0.678 (95% CI: 0.501–0.809) and 0.969 (95% CI: 0.920–0.989), respectively. The summary false positive rate was 0.031 (95% CI 0.011–0.071). Conclusion: In this meta-analysis, we found that CT had indeterminate sensitivity and excellent specificity for the diagnosis of THVI.

Dir Journal Diagnostic and Interventional Radiology