Trained and developed on over 35,000 low-dose computed tomography (LDCT) scans and validated in three independent data sets, a deep learning algorithm demonstrated an average area under the curve (AUC) of 90.6 percent for predicting lung cancer within one year.
Reportedly receiving the first Current Procedural Terminology (CPT) III code from the American Medical Association (AMA) for artificial intelligence (AI)-enabled brain magnetic resonance imaging (MRI)…
Powered by deep learning technology, Sonic DL reportedly facilitates the acquisition of cardiac magnetic resonance imaging (MRI) scans at 12 times the speed of conventional…
The use of contrast-enhanced computed tomography (CECT) was associated with more than double the risk of contrast-induced acute kidney injury (CI-AKI) in patients with diabetes…
Researchers found that a deep convolutional neural network, incorporating B-mode ultrasound and shear wave elastography data, had nearly an 88 percent sensitivity rate for predicting…
Five artificial intelligence (AI) algorithms for mammography assessment were better at predicting breast cancer risk over five years than the Breast Cancer Surveillance Consortium (BCSC)…
FIRMM-pix, a brain magnetic resonance imaging (MRI) software module recently launched at the International Society for Magnetic Resonance in Medicine (ISMRM) conference, reportedly employs visual…
For the detection of obstructive coronary artery disease (CAD), stress cardiovascular magnetic resonance imaging (MRI) demonstrated a sensitivity rate of 81 percent and a specificity…
Researchers found that a deep convolutional neural network, incorporating B-mode ultrasound and shear wave elastography data, had nearly an 88 percent sensitivity rate for predicting…
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In a large retrospective study of 26,455 participants from the National Lung Screening Trial, low dose computed tomography (LDCT) exams revealed significant incidental findings (SIFs),…
