Technology

Catch up on the top AI-related news and research in radiology over the past month.

For patients who had neuroradiological trauma CT scans, researchers noted no significant visual differences between deep learning denoising at 25% mAs and iterative reconstruction at 100% mAs.

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

The CINA-CSpine AI software reportedly demonstrated a 90.3 percent sensitivity rate for cervical spine fractures in validation testing on more than 300 non-contrast CT scans.

Emphasizing multi-structure segmentation and feature extraction from chest CT scans, an emerging AI model demonstrated an approximately 70 percent AUC for predicting significant incidental extrapulmonary findings as well as two-year and 10-year all-cause mortality.

Providing automated TI-RADS classifications and worksheets, the new AI-enabled software may facilitate improved efficiency with thyroid ultrasound exams.

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

A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.

Adjunctive AI showed no difference in accuracy than unassisted radiologists for intracranial hemorrhage (ICH) detection and had a slightly longer mean report turnaround time for ICH-positive cases, according to newly published prospective research.

Offering enhanced deep learning technology, the updated NeuroQuant 5.0 software reportedly bolsters segmentation capabilities for amyloid-related imaging abnormalities (ARIA) in patients with Alzheimer’s disease.

The AI-enabled software Rayvolve reportedly demonstrated a 96 percent sensitivity rate for diagnosing pediatric fractures in a recent study involving 3,000 patients.

Utilizing a new machine learning model, the OptimMRI software may improve radiosurgery applications and lesioning techniques such as MRI-guided focused ultrasound through enhanced targeting of the inferolateral part of the ventral intermediate nucleus (VIM).

Catch up on the most-well viewed radiology content in August 2024.

Identifying over 23 percent of interval breast cancers with a 96 percent sensitivity in mammography interpretation, an emerging AI software also facilitated correct localization in over 75 percent of cases involving interval breast cancer, according to new research.

Catch up on the latest research in neuroradiology over the past month.

Catch up on the top AI-related news and research in radiology over the past month.

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

Combining four CAD modules for valvular pathologies with a variety of automated measurements, the AI-enabled AISAP Cardio ultrasound system reportedly facilitates up to a 90 percent accuracy rate in detecting common cardiac conditions.

The AI-powered qCT LN Quant software reportedly generates 2D and 3D reconstructions and facilitates assessment of morphologic data across multiple thoracic studies.

Key features of the HealthCCSng V2.0 software include numerical coronary artery calcium (CAC) scoring, a new zero CAC category and user customization of upper and lower limits for CAC scoring categories.

Providing an 85.7 percent sensitivity rate for detecting cerebral aneurysms on computed tomography angiography (CTA), the deep learning model also offered a similar AUC (93 percent) in comparison to radiology reports (91 percent).

New research suggests the use of an AI software may help rule out pathology on over 50 percent of unremarkable chest X-rays with a 98 percent sensitivity threshold.

Offering a variety of AI features for streamlined workflow and enhanced visualization, the Acuson Origin ultrasound system also features the newly FDA-cleared AcuNav Lumos 4D ICE (intracardiac echocardiography) catheter.

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

At a 90 percent threshold for sensitivity, Lunit’s Insight CXR and the Nexus CXR software from Nexus demonstrated the highest specificity rates for tuberculosis (TB) detection in a patient population with a high prevalence of TB and HIV.

External validation testing revealed a deep learning combination of breast MRI, ultrasound and clinical factors had a 10 percent higher AUC for predicting axillary lymph node metastasis than sole use of MRI- or ultrasound-based deep learning models in patients with breast cancer.

New research shows the use of adjunctive AI resulted in a 12 percent higher sensitivity rate for lung nodule detection in comparison to radiologists without AI.

The first radiology triage modality to garner a Breakthrough Device Designation from the FDA, Annalise-Obstructive Hydrocephalus has reported sensitivity and specificity rates of 97.5 percent and 95.3 percent respectively.

Catch up on the top AI-related news and research in radiology over the past month.

Featuring the previously FDA-cleared BioTraceIO Vision and BioTraceIO Precision modalities, the BioTrace software suite combines real-time ultrasound guidance and advanced AI technology to help bolster outcomes with liver tumor ablation therapy.