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Enhanced Point‐of‐Care Ultrasound Applications by Integrating Automated Feature‐Learning Systems Using Deep Learning
Author(s) -
Shokoohi Hamid,
LeSaux Maxine A.,
Roohani Yusuf H.,
Liteplo Andrew,
Huang Calvin,
Blaivas Michael
Publication year - 2019
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.1002/jum.14860
Subject(s) - medicine , modalities , point of care ultrasound , artificial intelligence , premise , feature (linguistics) , automation , deep learning , medical physics , machine learning , ultrasound , computer science , radiology , mechanical engineering , social science , linguistics , philosophy , sociology , engineering
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.