对比度(视觉)
背景(考古学)
边疆
计算机科学
人工智能
图像对比度
领域(数学)
数据科学
政治学
数学
生物
古生物学
法学
纯数学
作者
Johannes Haubold,René Hosch,Gregor Jošt,Felix Kreis,Michael Forsting,Hubertus Pietsch,Felix Nensa
标识
DOI:10.1097/rli.0000000000001028
摘要
Abstract Artificial intelligence (AI) techniques are currently harnessed to revolutionize the domain of medical imaging. This review investigates 3 major AI-driven approaches for contrast agent management: new frontiers in contrast agent dose reduction, the contrast-free question, and new applications. By examining recent studies that use AI as a new frontier in contrast media research, we synthesize the current state of the field and provide a comprehensive understanding of the potential and limitations of AI in this context. In doing so, we show the dose limits of reducing the amount of contrast agents and demonstrate why it might not be possible to completely eliminate contrast agents in the future. In addition, we highlight potential new applications to further increase the radiologist's sensitivity at normal doses. At the same time, this review shows which network architectures provide promising approaches and reveals possible artifacts of a paired image-to-image conversion. Furthermore, current US Food and Drug Administration regulatory guidelines regarding AI/machine learning–enabled medical devices are highlighted.
科研通智能强力驱动
Strongly Powered by AbleSci AI