Enhanced Point-of-Care Ultrasound Applications by Integrating Automated Feature-Learning Systems Using Deep Learning.
artificial intelligence
deep learning
machine learning
point-of-care ultrasound
Journal
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
ISSN: 1550-9613
Titre abrégé: J Ultrasound Med
Pays: England
ID NLM: 8211547
Informations de publication
Date de publication:
Jul 2019
Jul 2019
Historique:
received:
04
08
2018
accepted:
30
09
2018
pubmed:
15
11
2018
medline:
8
1
2020
entrez:
15
11
2018
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1887-1897Informations de copyright
© 2018 by the American Institute of Ultrasound in Medicine.