2020
DOI: 10.1002/emp2.12018
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DIY AI, deep learning network development for automated image classification in a point‐of‐care ultrasound quality assurance program

Abstract: Presented at 2019 ACEP Scientific Assembly at #344. Funding and support: By JACEP Open policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist. AbstractBackground: Artificial intelligence (AI) is increasingly a part of daily life and offers great possibilities to enrich health care. Imaging ap… Show more

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Cited by 11 publications
(8 citation statements)
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“…Prior examples of POCUS ultrasound image augmentations have included modification of both still and dynamic images. 37 In one example researchers increased low numbers of adult inferior vena cava videos in a study on ML algorithm design for assessment of patient fluid responsiveness when being resuscitated for sepsis in the emergency department or intensive care unit. 16 Video rotations and flips allowed the researchers to convert a 191 video dataset into 3820 videos used for algorithm training.…”
Section: Discussionmentioning
confidence: 99%
“…Prior examples of POCUS ultrasound image augmentations have included modification of both still and dynamic images. 37 In one example researchers increased low numbers of adult inferior vena cava videos in a study on ML algorithm design for assessment of patient fluid responsiveness when being resuscitated for sepsis in the emergency department or intensive care unit. 16 Video rotations and flips allowed the researchers to convert a 191 video dataset into 3820 videos used for algorithm training.…”
Section: Discussionmentioning
confidence: 99%
“…Automatic and quantitative scoring system was developed to integrate and standardize the clinical assessment of the lung ( 72 , 73 ). Classification of benchmark ultrasound images is required for training a convolutional artificial network ( 74 ). The 5G-powered robot-assisted teleultrasound diagnostic system has already been applied in intensive care units ( 75 ), which has an advantage as easy operation, good feasibility, comparable diagnosis, and deserves to promote widely in clinical practice.…”
Section: Methodsmentioning
confidence: 99%
“…Another research area where DL is making significant progress is in improving the quality of image acquisition using POCUS [ 105 ]. Blaivas et al [ 106 ] developed a DL-based model for image quality assurance for automatic image classification. They used a large dataset of 121,000 images extracted from US sequences and had an accuracy of 98%.…”
Section: Advanced Us Imaging In Cardiology and DL Techniquesmentioning
confidence: 99%