Deep learning algorithms for detecting and visualising intussusception on plain abdominal radiography in children: a retrospective multicenter study - Kwon, Gitaek; Ryu, Jongbin; Oh, Jaehoon; Lim, Jongwoo; Kang, Bo kyeong; Ahn, Chiwon; Bae, Junwon; Lee, Dong Keon (Scientific Reports, 2020)
This study aimed to verify a deep convolutional neural network (CNN) algorithm to detect intussusception in children using a human-annotated data set of plain abdominal X-rays from affected children. From January 2005 to August 2019, 1449 images were collected from plain abdominal X-rays of patients ≤ 6 years old who were diagnosed with intussusception while 9935 images were collected from patients without intussusception from three tertiary academic hospitals (A, B, and C data sets). Single Shot MultiBox Detector and ResNet were used for abdominal detection and intussusception classification, respectively. The diagnostic performance of the algorithm was analysed using internal and external validation tests. The internal test values after training with two hospital data sets were 0.946 to 0.971 for the area under the receiver operating characteristic curve (AUC), 0.927 to 0.952 for the highest accuracy, and 0.764 to 0.848 for the highest Youden index. The values from external test using the remaining data set were all lower (P-value < 0.001). The mean values of the internal test with all data sets were 0.935 and 0.743 for the AUC and Youden Index, respectively. Detection of intussusception by deep CNN and plain abdominal X-rays could aid in screening for intussusception in children.
Keywords: Abdomen; Algorithms; Area Under Curve; Child, Preschool; Deep Learning; Diagnostic Tests, Routine; Female; Humans; Infant; Infant, Newborn; Intussusception; Male; Mass Screening; Neural Networks, Computer; Radiographic Image Interpretation, Computer-Assisted; Radiography, Abdominal; Reproducibility of Results; Retrospective Studies; ROC Curve