OBJECTIVES: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a m...
OBJECTIVES: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA).
OBJECTIVES: Anterior communicating artery (ACOM) aneurysms are the most common intracranial aneurysms, and predicting their rupture risk is challenging. We aimed to predict this risk using a two-layer feed-forward artificial neural network (ANN).
OBJECTIVES: We aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomograph...
OBJECTIVE: To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC).
OBJECTIVE: To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).
OBJECTIVE: This study aimed to assess the technical success, radiation dose, safety and performance level of liver thermal ablation using a computed tomography (CT)-guided robotic positioning system.
OBJECTIVE: To evaluate and compare novel robotic guidance and manual approaches based on procedural accuracy, procedural time, procedural performance, image quality as well as patient dose during image-guided microwave thermoablation.
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