乳房磁振造影
乳房成像
乳腺摄影术
工作流程
医学
磁共振成像
乳腺超声检查
数字乳腺摄影术
乳腺癌
医学物理学
放射科
计算机科学
人工智能
内科学
癌症
数据库
作者
Roberto Lo Gullo,Joren Brunekreef,Eric Marcus,Lynn Han,Sarah Eskreis‐Winkler,Sunitha B. Thakur,Ritse M. Mann,Kevin B. W. Groot Lipman,Jonas Teuwen,Katja Pinker
摘要
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI‐enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI‐enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. Level of Evidence 5 Technical Efficacy Stage 6
科研通智能强力驱动
Strongly Powered by AbleSci AI