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.

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This study aimed to assess the feasibility of shear-wave elastography (SWE) in testicular hematologic malignancy in children and young adults.Materials and Methods: A total of eight patients (mean age, 11.0 years; range, 0.8-20 years) with biopsy confirmed testicular hematologic malignancy between January 2018 and December 2020 were retrospectively evaluated. Multiparametric ultrasound examinations, including grayscale, color Doppler ultrasound (CDUS), and SWE, were performed. The stiffness was measured on the involved testicular area and contralateral normal parenchyma and if there was bilateral testicular involvement, the stiffness of involved area and adjacent normal echoic parenchyma was measured on one-sided testis. The Mann-Whitney U test was used for comparison of the stiffness.Results: On grayscale, the testicular lesions were noted as a solitary mass in one patient, multiple lesions in four patients, and diffuse involvement in three patients. On CDUS and SWE, all patients demonstrated increased vascularity, and the stiffness of the involved area was higher than the values of normal parenchyma (the involved area vs. normal parenchyma, 11.6 kPa [3.9-20.2 kPa] vs. 2.9 kPa [1.1-3.7 kPa], p= 0.003). The ratio of stiffness between the involved area and normal parenchyma was 3.4, ranging from 1.9 to 5.1. One patient showed decreased stiffness on follow-up SWE after treatment (initial; 7.0 mm3 vs. 1.0 mm3, 1 year later; 3.2 mm3 vs. 2.1 mm3, affected testis vs. normal testis). Conclusion:The increased testicular stiffness on SWE in children and young adults with hematologic malignancy suggests the possibility of testicular involvement.

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Background A high false-negative rate has been reported for the diagnosis of ossification of the posterior longitudinal ligament (OPLL) using plain radiography. We investigated whether deep learning (DL) can improve the diagnostic performance of radiologists for cervical OPLL using plain radiographs. Materials and methods The training set consisted of 915 radiographs from 207 patients diagnosed with OPLL. For the test set, we used 200 lateral cervical radiographs from 100 patients with cervical OPLL and 100 patients without OPLL. An observer performance study was conducted over two reading sessions. In the first session, we compared the diagnostic performance of the DL-model and the six observers. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) at the vertebra and patient level. The sensitivity and specificity of the DL model and average observers were calculated in per-patient analysis. Subgroup analysis was performed according to the morphologic classification of OPLL. In the second session, observers evaluated the radiographs by referring to the results of the DL-model. Results In the vertebra-level analysis, the DL-model showed an AUC of 0.854, which was higher than the average AUC of observers (0.826), but the difference was not significant (p = 0.292). In the patient-level analysis, the performance of the DL-model had an AUC of 0.851, and the average AUC of observers was 0.841 (p = 0.739). The patient-level sensitivity and specificity were 91% and 69% in the DL model, and 83% and 68% for the average observers, respectively. Both the DL-model and observers showed decreases in overall performance in the segmental and circumscribed types. With knowledge of the results of the DL-model, the average AUC of observers increased to 0.893 (p = 0.001) at the vertebra level and 0.911 (p < 0.001) at the patient level. In the subgroup analysis, the improvement was largest in segmental-type (AUC difference 0.087; p = 0.002). Conclusions The DL-based OPLL detection model can significantly improve the diagnostic performance of radiologists on cervical radiographs.