Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.

OBJECTIVE: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. MATERIALS AND METHODS: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). RESULTS: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). CONCLUSION: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

Computed tomography scan usage when US results are non-diagnostic for suspected acute appendicitis in children.

This retrospective study was aimed to determine the factors suggesting the need for computed tomography (CT) scanning when ultrasound (US) imaging results are negative or non-diagnostic in children suspicious for acute appendicitis in the emergency department.Patients less than 18 years old who underwent abdominal ultrasound and CT to rule out acute appendicitis were enrolled. Patients were classified into 2 groups: the false-negative group, in which patients had negative or non-diagnostic results on the initial US and a final diagnosis of acute appendicitis on the following abdominal CT, and the true-negative group, in which patients had negative or non-diagnostic US results and were negative on abdominal CT. Logistic regression and propensity score matching with the predicting factors were performed.The presence of vomiting (odds ratio (OR), 7.78; 95% confidence interval (CI), 1.92-41.04) and poor oral intake (OR, 4.67; 95% CI, 1.21-21.15) with a high white blood cell (WBC) count (OR 1.26; 95% CI, 1.09-2.37), segmented neutrophil ratio (OR, 1.09; 95% CI, 1.03-1.16), and C-reactive protein (CRP) (OR, 1.49; 95% CI, 1.09-2.37) were suggestive of the false-negative group. The propensity-matched population also showed significant associations with vomiting (OR, 7.86; 95% CI, 1.65-37.40) and poor oral intake (OR, 5.50; 95% CI, 1.28-23.69) with an elevated WBC count (OR, 1.27; 95% CI, 1.08-1.50), segmented neutrophil ratio (OR, 1.09; 95% CI, 1.03-1.16), and CRP (OR, 1.51; 95% CI, 1.03-2.22).A CT scan should be considered in children with suspected acute appendicitis if they have vomiting, high CRP, and high WBC count, despite negative or non-diagnostic US results.