AIMC Topic: Deep Learning
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Preliminary phantom study of four-dimensional computed tomographic angiography for renal artery mapping: Low-tube voltage and low-contrast volume imaging with deep learning-based reconstruction.
Radiography (London, England : 1995)
Mar 26, 2025
INTRODUCTION: A hybrid angio-CT system with 320-row detectors and deep learning-based reconstruction (DLR), provides additional imaging via 4D-CT angiography (CTA), potentially shortening procedure time and reducing DSA acquisitions, contrast media, ...
IR-GPT: AI Foundation Models to Optimize Interventional Radiology.
Cardiovascular and interventional radiology
Mar 26, 2025
Foundation artificial intelligence (AI) models are capable of complex tasks that involve text, medical images, and many other types of data, but have not yet been customized for procedural medicine. This report reviews prior work in deep learning rel...
GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.
Pharmaceutical research
Mar 26, 2025
PURPOSE: The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syn...
Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group A .
Journal of microbiology and biotechnology
Mar 26, 2025
Group A (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these finding...
Deep learning based quantitative cervical vertebral maturation analysis.
Head & face medicine
Mar 26, 2025
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...
Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort.
Arthritis research & therapy
Mar 26, 2025
BACKGROUND: Undifferentiated arthritis (UA) often develops into rheumatoid arthritis (RA), but predicting disease progression from seronegative UA remains challenging because seronegative RA often does not meet the classification criteria. This study...
Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.
BMC medical imaging
Mar 26, 2025
BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focu...
Exploring the categories of students' interest and their relationships with deep learning in technology supported environments.
Scientific reports
Mar 26, 2025
Interest is not only the starting point to begin a wonderful learning journey for students, but also an important driver for deep learning and continuous progress. This study used latent profile analysis (LPA), multiple logistic regression analysis, ...
Enhancing registration accuracy and eminence of multispectral transmission breast images by fusing multi-wavelength gen using vision transformer and LSTM.
Scientific reports
Mar 26, 2025
Enduring studies in the field of early breast cancer screening are investigating the use of multispectral transmission imaging. The frame accumulation system handles multispectral transmission images with deprived grayscale and unsatisfactory resolut...
Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis.
Scientific reports
Mar 26, 2025
COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like intensive care unit beds and ventilation equipment. Many prognostic statistical tools have been devel...
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