Background Accurate identification of ovarian cancer (OC) is of paramount importance in
clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the …

Background To develop an artificial intelligence (AI) model with radiomics and deep
learning (DL) features extracted from CT images to distinguish benign from malignant …

In recent years, the wide application of artificial intelligence (AI) has dramatically improved
the work efficiency of clinicians and reduced their workload. This review provides a glance at …

Objectives To develop and test the performance of computerized ultrasound image analysis
using deep neural networks (DNNs) in discriminating between benign and malignant …

Background Deep learning (DL) algorithms could improve the classification of ovarian
tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated …

This study evaluates the quality of published research using artificial intelligence (AI) for
ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of …

Objectives There currently lacks a noninvasive and accurate method to distinguish benign
and malignant ovarian lesion prior to treatment. This study developed a deep learning …

Background/Aim: This study aimed to use artificial intelligence (AI) to predict the pathological
diagnosis of ovarian tumors using patient information and data from preoperative …

Background Upon the discovery of ovarian cysts, obstetricians, gynecologists, and
ultrasound examiners must address the common clinical challenge of distinguishing …

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we
developed a convolutional neural network model with a convolutional autoencoder (CNN …