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

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

SUBMITTER: Chen X 

PROVIDER: S-EPMC10090215 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature