AIMC Topic: Radiation Exposure

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Comparative analysis of robotic assisted vs. traditional spinal angiography in a large single-center experience.

Journal of the neurological sciences
BACKGROUND: Spinal angiography (SA) remains the gold standard for evaluating spinal cord vasculature, but traditional approaches expose operators and patients to significant ionizing radiation. Robotic-assisted platforms offer potential advantages th...

Evaluation of an Autonomous Robotic System for Reducing Radiation Risk in a Real-World Cardiac Imaging Laboratory.

Journal of medical systems
BACKGROUND: Nuclear imaging is the cornerstone of clinical practice across many disciplines. Few innovations in imaging have addressed occupational health of radiographers exposed to radiation in their daily work. In this proof-of-concept study, we h...

Intracellular lymphocyte protein biomarkers for early radiological triage in the human population.

PloS one
In the event of a large-scale radiological or nuclear emergency, a rapid, high-throughput screening tool will be essential for efficient triage of potentially exposed individuals, optimizing scarce medical resources and ensuring timely care. The obje...

Evaluating Large Language Models for imaging modality selection: Potential to reduce unnecessary contrast agent use and radiation exposure.

Clinical imaging
INTRODUCTION: Large Language Models (LLMs) represent a transformative leap in artificial intelligence with the potential to revolutionize radiologic decision-making. This study uniquely evaluates the performance of various LLMs from different vendors...

Reduction of radiation exposure in chest radiography using deep learning-based noise reduction processing: A phantom and retrospective clinical study.

Radiography (London, England : 1995)
INTRODUCTION: Intelligent noise reduction (INR), a deep learning-based noise reduction developed by Canon, is used in planar radiography to improve image quality and reduce patient exposure dose. This study aimed to evaluate the reduction of patient ...

Power absorption and temperature rise in deep learning based head models for local radiofrequency exposures.

Physics in medicine and biology
Computational uncertainty and variability of power absorption and temperature rise in humans for radiofrequency (RF) exposure is a critical factor in ensuring human protection. This aspect has been emphasized as a priority. However, accurately modeli...

Research on predicting radiographic exposure time in imaging based on neural network prediction models.

Clinical neurology and neurosurgery
OBJECTIVE: To explore the anatomical and clinical factors that affect the radiographic exposure time in radial artery cerebral angiography and to establish a model.

The SINFONIA project repository for AI-based algorithms and health data.

Frontiers in public health
The SINFONIA project's main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental effects of radiation exposure on patients, workers, caretakers, and comforters, the public, and the env...

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

BMC medical informatics and decision making
INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no pr...

Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction.

European journal of radiology
BACKGROUND: Neck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished dia...