Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning
Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning
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DOI：
10.1002/jum.14860
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发表时间：
2019-07-01
影响因子：
2.3
通讯作者：
Blaivas, Michael
中科院分区：
文献类型：
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作者：
Shokoohi, Hamid;LeSaux, Maxine A.;Blaivas, Michael
人工智能（AI）和深度学习（DL）在医疗保健领域的最新应用包括增强的诊断成像模式，以支持临床决策并改善患者的预后。专注于使用基于DL的自动化系统来改进床旁超声（POCUS），我们将基于DL的自动化视为在各种临床环境中扩展和改进POCUS应用的关键领域。一个有前途的附加价值将是能够自动化培训模型选择，用于向医学学员和新手sonologist教授POCUS。POCUS应用程序和超声设备的多样性，每个都需要专门的AI模型和领域专业知识，限制了DL作为通用解决方案的使用。在本文中，我们重点介绍了人工智能在POCUS中最先进的潜在应用，这些应用针对自动图像解释中的高产量模型而定制，前提是提高POCUS扫描的准确性和有效性。
Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DL-based systems to improve point-of-care ultrasound (POCUS), we look at DL-based automation as a key field in expanding and improving POCUS applications in various clinical settings. A promising additional value would be the ability to automate training model selections for teaching POCUS to medical trainees and novice sonologists. The diversity of POCUS applications and ultrasound equipment, each requiring specialized AI models and domain expertise, limits the use of DL as a generic solution. In this article, we highlight the most advanced potential applications of AI in POCUS tailored to high-yield models in automated image interpretations, with the premise of improving the accuracy and efficacy of POCUS scans.