AI

Catch up on the top radiology content of the past week.

Artificial intelligence (AI) accurately diagnosed 79 percent of proximal large vessel occlusions with an ipsiversive gaze deviation on non-contrast computed tomography (CT), according to new research findings presented recently at the Society of Neurointerventional Surgery’s (SNIS) 19th Annual Meeting in Toronto.

Catch up on the top radiology content of the past week.

The next-generation ultrasound system reportedly offers enhanced images through graphic-based beamforming technology, a variety of artificial intelligence (AI)-powered tools and reduced exam times.

RapidAI’s Rapid Hyperdensity tool reportedly allows quicker assessment of hyperdense tissue in the brain via non-contrast computed tomography (CT) scans.

In a video interview, Morris Panner, the president of Intelerad Medical Systems, discussed key observations from the recent Society for Imaging Informatics in Medicine (SIIM) conference, recent research about artificial intelligence (AI) adoption and emerging goals for enhancing the efficiency of radiology workflows.

Catch up on the top radiology content of the past week.

The artificial intelligence (AI) capabilities of the new software reportedly facilitate scanning times that are three times faster than conventional magnetic resonance image (MRI) scanners.

An updated machine learning model demonstrated a 23 percent improvement in accuracy and a 36.1 percent improvement in sensitivity over visual radiologist assessment of ultrasound images for differentiating malignant lymph nodes and benign COVID-19 vaccination-related changes to lymph nodes.

Catch up on the top radiology news of the past week.

The study, involving 500 patients, showed that artificial intelligence (AI) assistance enhanced fracture diagnosis on radiographs and reduced reading time for radiologists of varying experience levels.

The new magnetic resonance imaging (MRI) device reportedly offers deep learning technologies and advanced processing of whole-body images in a cost-effective and lightweight model.

Ambient speech capabilities in emerging voice recognition products and software updates may convert the clinical context of conversational speech into structured data for radiology reports.

Calantric™ Digital Solutions reportedly offers artificial intelligence (AI)-powered apps, bolsters lesion detection, facilitates triage priorities and enhances workflow efficiency.

Catch up on the top radiology news of the past week.

New research suggests that an emerging predictive biomarker, derived from a combination of magnetic resonance imaging (MRI) brain scans and a machine learning algorithm, has significantly greater accuracy than previously established measurements for diagnosing Alzheimer’s disease.

In a recent video interview, abdominal radiologist Sonia Gupta, MD discussed key principles in assessing potential alliances with artificial intelligence (AI) vendors and the potential of AI to alleviate the time-consuming, administrative aspects of patient care.

Catch up on the top radiology content of the past week.

Noting that an AI software platform could save radiologists up to an hour a day in interpreting chest computed tomography (CT) scans, the authors of a prospective study found shorter mean interpretation times with non-contrast and contrast-enhanced CT as well as positive CT scans with and without significant new findings.

Catch up on the top radiology content of the past week.

The deep learning model may offer enhanced sensitivity and specificity on MRI for patients with glioblastoma, according to preliminary research presented at the Society for Imaging Informatics in Medicine (SIIM) conference.

A 3D whole brain convolutional neural network could provide enhanced sensitivity and specificity for diagnosing intracranial hemorrhages on computed tomography, according to new research presented at the Society for Imaging Informatics in Medicine (SIIM) conference in Kissimmee, Fla.

Catch up on the top radiology content of the past week.

Deep radiomics models that included deep learning features had a 40 percent or greater increase in the specificity rate for diagnosing osteoporosis on hip radiographs in comparison to models that only emphasized clinical and/or textural features.

The new artificial intelligence-powered software reportedly helps detect central pulmonary embolism (PE) on computed tomography pulmonary angiogram (CTPA) images and streamlines communication among interventional teams to bolster treatment outcomes.