Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images

Xiong Chen; Guochang You; Qinchang Chen; Xiangxiang Zhang; Na Wang; Xuehua He; Liling Zhu; Zhouzhou Li; Chen Liu; Shixiang Yao; Junshuang Ge; Wenjing Gao; Hongkui Yu

doi:10.1016/j.isci.2023.106456

Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images

iScience. 2023 Mar 21;26(4):106456. doi: 10.1016/j.isci.2023.106456. eCollection 2023 Apr 21.

Authors

Xiong Chen^{1

2}, Guochang You³, Qinchang Chen⁴, Xiangxiang Zhang⁵, Na Wang⁵, Xuehua He⁵, Liling Zhu⁵, Zhouzhou Li⁵, Chen Liu⁵, Shixiang Yao⁵, Junshuang Ge⁶, Wenjing Gao⁶, Hongkui Yu⁵

Affiliations

¹ Department of Paediatric Urology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China.
² Department of Paediatric Surgery, Guangzhou Institute of Paediatrics, Guangzhou Medical University, Guangzhou 510623, P. R. China.
³ Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, P. R. China.
⁴ Department of Pediatric Cardiology, Guangdong Provincial Key Laboratory of Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute, Guangzhou 510080, P. R. China.
⁵ Department of Ultrasound, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China.
⁶ Clinical Data Center, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, P. R. China.

Abstract

Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children.

Keywords: Artificial intelligence; Computer-aided diagnosis method; Medical imaging.