Bone pelvic dimensions and body habitus may have effects on robot-assisted radical prostatectomy (RARP). In this study, we examined the effects of body mass index, bone pelvis measurements and prostate measurements on console time (CT), decrease in p...
Our aim is to define the capabilities of radiomics and machine learning in predicting pseudoprogression development from pre-treatment MR images in a patient cohort diagnosed with high grade gliomas. In this retrospective analysis, we analysed 131 pa...
OBJECTIVES: The aim of this study was to evaluate the image quality and diagnostic performance of a deep-learning (DL)-accelerated two-dimensional (2D) turbo spin echo (TSE) MRI of the knee at 1.5 and 3 T in clinical routine in comparison to standard...
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients' life. The emerging computer-aided diagnostic meth...
OBJECTIVES: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficiency.
Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus,...
OBJECTIVE: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging...
OBJECTIVES: To implement a pipeline to automatically segment the ROI and to use a nomogram integrating the MRI-based radiomics score and clinical variables to predict responses to neoadjuvant chemotherapy (NAC) in osteosarcoma patients.
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