In a study of over 20,700 people, researchers found that artificial intelligence (AI) analysis of body composition measurements via lung cancer screening computed tomography (CT) exams improves the prediction of mortality risks for lung cancer, cardiovascular disease, and all-cause mortality.
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An artificial intelligence model, trained on MRI and FLAIR imaging from over 900 patients with multiple sclerosis, demonstrated a 96 percent accuracy rate and 99 percent specificity rate for contrast-enhancing lesions in this patient population.
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An artificial intelligence (AI)-enabled platform that can reportedly diagnose heart failure with preserved ejection fraction (HFpEF) through analysis of a single echocardiogram view, the EchoGo Heart Failure now has a HCPCS code for use of the technology in outpatient settings for Medicare beneficiaries.
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) software, Icometrix says its adjunctive quantification software can be utilized for diagnosis and assessment of conditions ranging from Alzheimer’s disease and epilepsy to stroke and dementia.
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The artificial intelligence (AI) software facilitates high-resolution three-dimensional computed tomography (CT) images of the breast.
In multiple mammography datasets with the original radiologist-detected abnormality removed, deep learning detection of breast cancer had an average area under the curve (AUC) of 87 percent and an accuracy rate of 83 percent, according to research presented at the recent Society for Imaging Informatics in Medicine (SIIM) conference.
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In a recent video interview from the Society for Imaging Informatics in Medicine (SIIM) conference, Ali Tejani, M.D., discussed pertinent insights on leveraging the value of adjunctive artificial intelligence (AI)-enabled triage software for computed tomography (CT) scans with radiology workflow improvements to achieve “clinically meaningful change” for patients with incidental pulmonary emboli findings.
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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 neoadjuvant chemotherapy response in patients with breast cancer.
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) risk model, according to new retrospective research involving over 13,000 women.
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 MRI systems.
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The artificial intelligence (AI)-powered Ezra Flash reportedly enhances magnetic resonance imaging (MRI) and enables significant reductions in scan times and costs for full-body MRI.
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Precision DL, a deep learning-based software which will be available on GE HealthCare’s Omni Legend PET/CT device, reportedly increases the detectability of small, low-contrast lesions by 42 percent.
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Emerging breast ultrasound research showed the use of computer-aided diagnosis (CAD), powered by deep learning, led to 24 percent and 36.9 percent improvements in accuracy and specificity, respectively, in the use of BI-RADS classifications by radiologists without breast ultrasound expertise.
Six reader studies on digital mammography revealed a pooled sensitivity rate of 80.8 percent for stand-alone artificial intelligence (AI) in comparison to 72.4 percent for radiologist assessment while seven historic cohort studies showed a 75.8 percent pooled sensitivity rate for stand-alone AI versus 72.6 percent for radiologist interpretation of digital mammography.
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Through artificial intelligence (AI), Neurophet AQUA reportedly provides segmentation and analysis of brain magnetic resonance imaging (MRI) for possible brain atrophy in five minutes.
