Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics
with the highest mortality among gynecological malignancies. Accurate and early diagnosis …

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

We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for
ovarian mass differential diagnosis. This single-center retrospective study included 1,054 …

Background Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian
cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed …

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 …

Objective: The objective of this study is to build a deep learning model with advanced
accuracy of differential diagnosis between malignant and benign lesions of ovary. Methods …

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

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

Study question Can the deep learning-based diagnostic algorithms of pelvic
ultrasonography be a useful clinical tool for evaluating the malignancy risk of ovarian …

The timely identification and management of ovarian cancer are critical determinants of
patient prognosis. In this study, we developed and validated a deep learning radiomics …