ABSTRACT
Gastrointestinal (GI) emergencies in neonates and infants encompass from the beginning to the end of the GI tract. Both congenital and acquired conditions can cause various GI emergencies in neonates and infants. Given the overlapping or nonspecific clinical findings of many different neonatal and infantile GI emergencies and the unique characteristics of this age group, appropriate imaging is key to accurate and timely diagnosis while avoiding unnecessary radiation hazard and medical costs. In this paper, we discuss the radiological findings of essential neonatal and infantile GI emergencies, including esophageal atresia and tracheoesophageal fistula, hypertrophic pyloric stenosis, duodenal atresia, malrotation, midgut volvulus for upper GI emergencies, and jejunoileal atresia, meconium ileus, meconium plug syndrome, meconium peritonitis, Hirschsprung disease, anorectal malformation, necrotizing enterocolitis, and intussusception for lower GI emergencies.
ABSTRACT
Objective@#To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. @*Materials and Methods@#Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. @*Results@#The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). @*Conclusion@#The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.
ABSTRACT
Objective@#To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. @*Materials and Methods@#Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. @*Results@#The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). @*Conclusion@#The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.
ABSTRACT
Purpose@#This study evaluated the diagnostic performance of contrast-enhanced voiding urosonography (ce-VUS) using a second-generation ultrasound contrast agent for the diagnosis of vesicoureteral reflux (VUR) and intrarenal reflux (IRR), and compared it with that of standard fluoroscopic voiding cystourethrography (VCUG). @*Methods@#Thirty-two consecutive children from April to October 2019 were included in this study. ce-VUS and VCUG were performed simultaneously by two operators with intravesical infusion of a mixture of ultrasound contrast medium, iodinated contrast medium and water. Two pediatric radiologists independently reviewed the ce-VUS and VCUG images and reported the presence and degree of VUR (grades I-V), and the presence and type of IRR. @*Results@#Twenty-seven of 63 urinary systems showed VUR. Interobserver agreement for VUR grading was very good for both examinations (κ=0.87; 95% confidence interval [CI], 0.82 to 0.92 for ce-VUS and κ=0.92; 95% CI, 0.87 to 0.96 for VCUG). The detection rate of VUR showed no significant difference between the two examinations (P=0.370). Four cases of VUR were missed on ce-VUS, while one case of VUR was missed on VCUG. All four false-negative cases on ce-VUS were grade 1 VUR. The two examinations showed very good agreement regarding VUR grading (κ =0.89; 95% CI, 0.81 to 0.96). IRR was more frequently detected with ce-VUS than with VCUG (10 cases with ce-VUS vs. 3 cases with VCUG, P=0.016). @*Conclusion@#ce-VUS showed very good agreement with VCUG for detecting grade 2 VUR and above, while grade 1 VUR was sometimes missed with ce-VUS. IRR was more frequently detected with ce-VUS than with VCUG.
ABSTRACT
Objective@#To evaluate the incidence and risk factors of emetic complications associated with the intravenous administration of low-osmolality iodinated contrast media (ICM) in children undergoing computed tomography (CT). @*Materials and Methods@#All children who underwent contrast-enhanced CT between April 2017 and July 2019 were included.Pediatric patients were instructed on the preparative dietary protocol at our institution. Experienced nurses in the radiology department monitored the children during the CT scans and recorded any emetic complications in their electronic medical records. These data were used to calculate the incidence of emetic complications. Various patient factors and technical factors, including fasting duration, the type and volume of ICM, and ongoing chemotherapy, were evaluated to identify risk factors for emetic complications using univariate and multivariate logistic regression analyses. @*Results@#Among the 864 children (mean age, 8.4 ± 5.7 years) evaluated, 18 (2.1%) experienced emetic complications (6 experienced nausea only and 12 experienced nausea and vomiting). None of the children developed aspiration pneumonia.The mean fasting duration of patients with emesis was 7.9 ± 5.7 hours (range, 3–21 hours), whereas that of patients without nausea was 8.7 ± 5.7 hours (range, 0–24 hours). Fasting duration was not associated with the development of nausea and vomiting (p = 0.634). Multivariate logistic regression analysis revealed that ongoing chemotherapy (odds ratio [OR] = 4.323;95% confidence interval [CI] = 1.430–13.064; p = 0.009), iomeprol use (OR = 7.219; 95% CI = 1.442–36.146; p = 0.016), and iohexol use (OR = 5.241; 95% CI = 1.350–20.346;p = 0.017) were independent risk factors for emetic complications. @*Conclusion@#Only a small proportion (2.1%) of children experienced nausea or vomiting after exposure to low-osmolality ICM.Many children underwent excessive fasting; however, fasting duration was not associated with nausea and vomiting. Moreover, ongoing chemotherapy and the use of iomeprol or iohexol were identified as potential risk factors for emetic complications in children.