Background: No research has been conducted on the use of deep learning for breastfeeding support.
Research Aim: This study aims to develop a nipple trauma evaluation system using deep learning.
Methods: We used an exploratory data analysis approach to develop a deep-learning model for medical imaging.
Radiol Phys Technol
December 2024
Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points.
View Article and Find Full Text PDFBackground: Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients.
Purpose: To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels.
Study Type: Retrospective.