AI

In a recent interview, Manisha Bahl, M.D., discussed key findings from a new study on AI and digital breast tomosynthesis that she presented at the Society for Breast Imaging (SBI) conference.

An artificial intelligence algorithm for dynamic contrast-enhanced breast MRI offered a 93.9 percent AUC for breast cancer detection, and a 92.3 percent sensitivity in BI-RADS 3 cases, according to new research presented at the Society for Breast Imaging (SBI) conference.

Offering expedited calculation of prostate volume, Clarius Prostate AI is reportedly the first AI-enabled prostate measurement tool to garner FDA clearance for use with handheld ultrasound.

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

In a recent interview, Benjamin Kann, M.D., discussed the use of an emerging AI model that can assess longitudinal MR imaging to help predict postoperative recurrence risk for gliomas in pediatric patients.

Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.

The T-Mode Anterior Knee feature reportedly offers a combination of automated segmentation and real-time conversion of grayscale ultrasound images into color-coded visuals that bolster understanding for novice ultrasound users.

In comparison to native 64-mT MRI, the deep learning generative model LowGAN offered enhanced white matter lesion conspicuity and image quality in a study involving patients with multiple sclerosis.

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

In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.

Artificial intelligence demonstrated higher AUC, sensitivity, and specificity than radiologists for detecting coronary artery stenosis > 50 percent on computed tomography angiography (CTA), according to a new 17-study meta-analysis.

The additional FDA 510(k) clearance for the AI-powered cmAngio platform covers use of the software for GE HealthCare mammography systems.

Demonstrating no significant difference with radiologist detection of clinically significant prostate cancer (csPCa), a biparametric MRI-based AI model provided an 88.4 percent sensitivity rate in a recent study.

Recent research has demonstrated that the AI software HeartFocus enabled novice health-care providers to achieve greater than 85 percent agreement with expert sonographers in assessing echocardiographic parameters.

In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).

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

The Bunkerhill BMD software provides AI-automated assessment of bone mineral density from non-contrast abdominal computed tomography scans.

Artificial intelligence (AI) software demonstrated a 92 percent sensitivity for detecting fractures in a study involving over 1,600 X-rays from a tertiary pediatric emergency department.

Going into effect in 2026, the new CPT codes may facilitate increased adoption of the CaRi-Heart software for detecting coronary inflammation from computed tomography scans pending FDA clearance of the technology.

While standalone AI interpretation offered over 10 percent higher sensitivity than radiologists for patients undergoing post-mastectomy surveillance mammography, it missed over 30 percent of breast cancers.

For patients with acute ischemic stroke, research has demonstrated that automated assessment of ischemic core volume on brain CT scans via the Brainomix 360 software is equivalent to that derived from CT perfusion.

In a study recently presented at the American College of Cardiology (ACC) conference, researchers found that novice use of AI-guided cardiac ultrasound after an AI-enabled electrocardiogram increased the positive predictive value for reduced left ventricular ejection fraction (LVEF) or aortic valve stenosis by 33 percent.

The Exa PACS/RIS platform reportedly combines AI-enabled worklist navigation tools with advances in multiplanar functionality and 3D-generated image segmentation.

Featuring enhanced low-dose image quality with motion-free images, the Revolution Vibe CT system reportedly facilitates improved diagnostic clarity for patients with conditions ranging from in-stent restenosis to atrial fibrillation.

The AI-powered AZchest CXR software reportedly offers 93.79 percent sensitivity and a 98.57 percent AUC for pneumothorax.