AJR. American journal of roentgenology
Feb 12, 2025
Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. The purpose of this study was to develop an artificial intelligence (AI) model to detect CRC on ro...
PURPOSE: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the Co...
OBJECTIVE: The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based ite...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 20, 2025
This study introduces the Deep Learning-based Cardiovascular Disease Incident (DL-CVDi) score, a novel biomarker derived from routine abdominal CT scans, optimized to predict cardiovascular disease (CVD) risk using deep survival learning. CT imaging,...
OBJECTIVES: To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this s...
Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of...
PURPOSE: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hy...
Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abd...
RATIONALE AND OBJECTIVES: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image q...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.