Low-dose CT detects kidney stones as well as high-dose
CT protocols used by researchers at Loma Linda University decreased radiation dose up to 95% when de-tecting distal ureteral calculi.
CT protocols used by researchers at Loma Linda University decreased radiation dose up to 95% when detecting distal ureteral calculi. The researchers used ultralow-dose CT as opposed to unenhanced multidetector CT.
A total of 85 calcium oxalate stones 3 to 7 mm long were prospectively placed in 14 human cadaveric distal ureters in 56 random configurations. The intact kidneys, ureters, and bladders were placed in the cadavers and CT was performed at 140, 100, 60, 30, 15, and 7.5 mA sec (J Urol 2009;182[6]:2762-2767).
Overall sensitivity and specificity were 98% and 83%. Imaging using 7.5 mA sec settings resulted in 97% sensitivity. Specificity was 84%.
Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee?
April 24th 2025Employing 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.
Meta-Analysis Shows Merits of AI with CTA Detection of Coronary Artery Stenosis and Calcified Plaque
April 16th 2025Artificial 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.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In 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).
