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 Over the past years, the application of artificial intelligence (AI) in medicine has
increased rapidly, especially in diagnostics, and in the near future, the role of AI in medicine …

Medical image analysis and disease diagnosis have significantly improved with the use of AI
and Machine Learning algorithms. Automated systems for medical image analysis will help …

Functionally graded materials (FGMs) have garnered significant attention in research due to
their potential for creating innovative materials with diverse applications. This study focuses …

One of the common types of cancer for women is ovarian cancer. Still, at present, there are
no drug therapies that can properly cure this deadly disease. However, early-stage detection …

With the development of machine learning and deep learning models, artificial intelligence
is now being applied to the field of medicine. In oncology, the use of artificial intelligence for …

Objectives The aim of this study was to compare the accuracy of seven classical Machine
Learning (ML) models trained with ultrasound (US) soft markers to raise suspicion of …

Background Assessing malignancy risk is important to choose appropriate management of
ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian …

Ovarian cysts are one of the most common gynecologic disorders encountered in clinical
practice. The pelvic computed tomography (CT) scan is a commonly employed examination …

Ovarian cancer, a deadly female reproductive system disease, is a significant challenge in
medical research due to its notorious lethality. Addressing ovarian cancer in the current …