November 7th 2024
An artificial intelligence (AI) model demonstrated a 72 percent AUC for predicting breast cancer one year before a subsequent MRI.
Artifact Reduction Drives Technology Advances with Updated Version of Echelon Synergy MRI System
October 30th 2024Emerging technologies included with the 10th version of the 1.5T MRI platform include Synergy DLR Clear and Synergy Vision that are geared toward mitigating common challenges with artifacts.
Study: AI Model Significantly Enhances CTA Workflow Efficiency and Detection for Cerebral Aneurysm
October 18th 2024Adjunctive use of deep learning reportedly led to a 37 percent reduction of interpretation time for cerebral aneurysm assessment on computed tomography angiography (CTA) and greater than a 90 percent reduction in post-processing time.
Can Innovations with AI Help Address the Impact of Staffing Shortages on Radiology Workflow?
October 7th 2024While staffing shortages in radiology continue to persist after the COVID-19 pandemic, current and emerging innovations powered by artificial intelligence (AI) may help facilities navigate these challenges and mitigate rising costs of health care.
AI Mammography Platform Shows Promising Results for Detecting Subclinical Breast Cancer
October 3rd 2024Mean artificial intelligence (AI) scoring for breasts developing cancer was double that of contralateral breasts at initial biennial screening and was 16 times higher at the third biennial screening, according to a study involving over 116,000 women with no prior history of breast cancer.
Can AI Enhance CT Detection of Incidental Extrapulmonary Abnormalities and Prediction of Mortality?
September 18th 2024Emphasizing 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.
Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality
September 6th 2024A 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.