Background And Aim: The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologists evaluating computed tomography (CT) scans. The necessity for automated diagnostic tools has been brought to light by the shortage of radiologists and the growing demand for rapid and accurate fracture diagnosis. Convolutional Neural Networks (CNNs) are a potential new class of medical imaging technologies that use deep learning (DL) to improve diagnosis accuracy.
View Article and Find Full Text PDFObjective: This study aimed to assess the diagnostic role of perfusion weighted image (DCE-PWI) to differentiate benign from malignant breast lesions.
Patients And Methods: The study comprised 32 women who had mammography and/or breast ultrasonography findings that were clinically questionable. All patients were fasting during the magnetic resonance imaging (MRI) test to avoid nausea or dynamic contrast-enhanced vomiting from the contrast medium.
Background: Researchers have recently focused on assessing the accuracy of diffusion-weighted magnetic resonance imaging (DW-MRI) in predicting pelvic lymph node metastases in gynecological malignancies.
Purpose: To evaluate the diagnostic value of DW-MRI in discriminating between metastatic and non-metastatic pelvic lymph nodes in endometrial cancer patients.
Material And Methods: This retrospective database study was conducted with 33 women aged 30-84 years with pathologically proven endometrial cancer that had been assessed by DW-MRI before their first treatment initiation at our referral hospital from March 2016 to April 2019.
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