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J Ultrasound Med. 2018 Nov 13. doi: 10.1002/jum.14860. [Epub ahead of print]

Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning.

Author information

1
Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
2
Department of Emergency Medicine, (George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
3
Platform Technology and Science, GlaxoSmithKline, Cambridge, Massachusetts, USA.
4
Department of Emergency Medicine, University of South Carolina School of Medicine, Columbia, South Carolina, USA.
5
St Francis Hospital, Columbus, Georgia, USA.

Abstract

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.

KEYWORDS:

artificial intelligence; deep learning; machine learning; point-of-care ultrasound

PMID:
30426536
DOI:
10.1002/jum.14860

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