Employing AI in Detecting Subdural Hematomas on Head CTs: An Interview with Jeremy Heit, MD, PhD
In a video interview from the International Stroke Conference (ISC), Jeremy Heit, M.D., Ph.D., discussed new research revealing over 90 percent sensitivity and specificity rates for AI detection of subdural hematomas on non-contrast-enhanced head CTs.
Comparative AI Study Shows Merits of RapidAI LVO Software in Stroke Detection
The Rapid LVO AI software detected 33 percent more cases of large vessel occlusion (LVO) on computed tomography angiography (CTA) than Viz LVO AI software, according to a new comparative study presented at the International Stroke Conference (ISC).
What a New Meta-Analysis Reveals About PET/CT Radiotracers for csPCa
The PET/CT agent 18F-PSMA-1007 offered the highest surface under the cumulative ranking curve (SUCRA) out of nine radiotracers at the patient and lesion level for detecting clinically significant prostate cancer (csPCa), according to a meta-analysis.
Study: Mammography AI Leads to 29 Percent Increase in Breast Cancer Detection
Use of the mammography AI software had a nearly equivalent false positive rate as unassisted radiologist interpretation and resulted in a 44 percent reduction in screen reading workload, according to findings from a randomized controlled trial involving over 105,000 women.
New CT Angiography Study Shows Impact of COVID-19 on Coronary Inflammation and Plaque
Prior COVID-19 infection was associated with a 28 percent higher progression of total percent atheroma volume (PAV) annually and over a 5 percent higher incidence of high-risk plaque in patients with coronary artery lesions, according to CCTA findings from a new study.
Pertinent Insights into the Imaging of Patients with Marfan Syndrome
Emphasizing the role of radiologists in facilitating timely diagnosis of Marfan syndrome, Alan Braverman, M.D. discussed the use of echocardiography, CT, and MRI in evaluating patients with this genetic aortic condition.
Computed Tomography Study Assesses Model for Predicting Recurrence of Non-Small Cell Lung Cancer
A predictive model for non-small cell lung cancer (NSCLC) recurrence, based on clinical parameters and CT findings, demonstrated an 85.2 percent AUC and 83.3 percent sensitivity rate, according to external validation testing in a new study.
Can MRI-Based Deep Learning Improve Risk Stratification in PI-RADS 3 Cases?
In external validation testing, a deep learning model demonstrated an average AUC of 87.6 percent for detecting clinically significant prostate cancer (csPCA) on prostate MRI for patients with PI-RADS 3 assessments.