Abstract
Hepatic cystic echinococcosis (HCE) is a zoonotic disease that occurs when the larvae of Echinococcus granulosus parasitize the livers of humans and mammals. HCE has five subtypes, and accurate subtype classification is critical for choosing a treatment strategy. To evaluate the clinical utility of artificial intelligence (AI) based on convolutional neural networks (CNNs) in the classification of HCE subtypes via ultrasound imaging, we collected ultrasound images from 4,012 HCE patients at the First Affiliated Hospital of Xinjiang Medical University between 2008 and 2020. Specifically, 1,820 HCE images from 967 patients were used as the training and validation sets for the construction of the AI model, and the remaining 6,808 images from 3,045 patients were used as the test set to evaluate the performance of the AI models. The 6,808 images were randomly divided into six groups, and each group contained equal proportions of the five subtypes. The data of each group were analyzed by a resident physician. The accuracy of HCE subtype classification by the AI model and by manual inspection was compared. The AI HCE classification model showed good performance in the diagnosis of subtypes CE1, CE2, CE4, and CE5. The overall accuracy of the AI classification (90.4%) was significantly greater than that of manual classification by physicians (86.1%; P <0.05). The CNN can better identify the five subtypes of HCE on ultrasound images and should help doctors with little experience in more accurately diagnosing HCE.