医学
工作流程
人工智能
深度学习
图像处理
人工神经网络
心脏成像
可穿戴计算机
医学物理学
机器学习
计算机科学
图像(数学)
放射科
数据库
嵌入式系统
作者
Manuel A. Morales,Warren J. Manning,Reza Nezafat
出处
期刊:Radiology
[Radiological Society of North America]
日期:2024-01-01
卷期号:310 (1)
被引量:7
标识
DOI:10.1148/radiol.231269
摘要
Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.
科研通智能强力驱动
Strongly Powered by AbleSci AI