COVID-Net US: A Tailored, Highly Efficient, Self-attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-Care Ultrasound Imaging

MacLean, Alexander; Abbasi, Saad; Ebadi, Ashkan; Zhao, Andy; Pavlova, Maya; Gunraj, Hayden; Xi, Pengcheng; Kohli, Sonny; Wong, Alexander

doi:10.1007/978-3-030-87722-4_18

Cited by 7 publications

(13 citation statements)

References 12 publications

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“…To evaluate the impact of the proposed extended linear-convex ultrasound augmentation learning strategy, we leveraged a newer version of the COVIDx-US benchmark dataset [2] in this study. This version of the benchmark dataset consists of 16,649 POCUS images, with 12,260 captured using convex probes and 4389 captured using linear probes, which we leverage fully in this study and is larger and more diverse than what was utilized in [3]. In terms of disease breakdown, 6764 of the POCUS images were from COVID-19 positive patients, 9985 were from COVID-19 negative patients.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning

Zeng¹,

Florea²,

Wong³

2022

Preprint

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Section: Experiments and Resultsmentioning

confidence: 99%

“…However, a major barrier with widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians that can interpret POCUS examinations for COVID-19 assessment [1]. Therefore, there has been a considerable interest in deep learningdriven clinical decision support systems [3].…”

Section: Introductionmentioning

confidence: 99%

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning

Zeng¹,

Florea²,

Wong³

2022

Preprint

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“…We believe accurate AI-driven solutions can be built on the digital images provided in the COVIDx-US dataset. In our very recent study [50], we built and introduced a highly efficient, self-attention deep convolutional neural network model using COVIDx-US v1.3. The model that is highly tailored for COVID-19 screening achieved an AUC of over 0.98 while achieving 353× lower architectural complexity, 62× lower computational complexity, and 14.3× faster inference times on a Raspberry Pi.…”

Section: Discussionmentioning

confidence: 99%

COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics

Ebadi¹,

Xi²,

MacLean³

et al. 2022

Front. Biosci. (Landmark Ed)

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Background:The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome play a key role in controlling the pandemic. Recent studies have highlighted the role of point-of-care ultrasound imaging for COVID-19 screening and prognosis, particularly given that it is non-invasive, globally available, and easy-to-sanitize. COVIDx-US Dataset: Motivated by these attributes and the promise of artificial intelligence tools to aid clinicians, we introduce COVIDx-US, an open-access benchmark dataset of COVID-19 related ultrasound imaging data. The COVIDx-US dataset was curated from multiple data sources and its current version, i.e., v1.5., consists of 173 ultrasound videos and 21,570 processed images across 147 patients with COVID-19 infection, non-COVID-19 infection, other lung diseases/conditions, as well as normal control cases. Conclusions: The COVIDx-US dataset was released as part of a large open-source initiative, the COVID-Net initiative, and will be continuously growing, as more data sources become available. To the best of the authors' knowledge, COVIDx-US is the first and largest open-access fully-curated benchmark lung ultrasound imaging dataset that contains a standardized and unified lung ultrasound score per video file, providing better interpretation while enabling other research avenues such as severity assessment. In addition, the dataset is reproducible, easy-to-use, and easy-to-scale thanks to the welldocumented modular design.

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“…It incorporates advancements such as additional layers, refined architecture, or enhanced training techniques to enhance the accuracy and reliability of COVID-19 detection from chest X-ray images. Two articles we reviewed in this study used COVID-Net US [77] and COVID-Net US-X [79].…”

Section: Covid-netmentioning

confidence: 99%

“…Using a hybrid network consisting of the InceptionV3 model to extract spatial information and recurrent neural network (RNN) for extracting temporal features, Azimi et al [73] did binary classification of lung ultrasound images into COVID-19 and non-COVID classes. MacLean et al [77] proposed a deep neural network, COVID-Net US, leveraging a generative synthesis process that finds an optimal macro-architecture design in classifying lung ultrasound images into COVID-19 and non-COVID classes. Furthermore, MacLean et al [78] used ResNet to classify lung ultrasound images into one of the four lung ultrasound severity scores (i.e., 0, 1, 2, 3).…”

Section: Studiesmentioning

confidence: 99%

Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

Morshed¹,

Shihab²,

Jahin³

et al. 2023

Preprint

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The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings to detect COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on ultrasound-based AI studies for COVID-19 detection that use public or private lung ultrasound datasets. We surveyed articles that used publicly available lung ultrasound datasets for COVID-19 and reviewed publicly available datasets and organize ultrasound-based AI studies per dataset. We analyzed and tabulated studies in several dimensions, such as data preprocessing, AI models, cross-validation, and evaluation criteria. In total, we reviewed 42 articles, where 28 articles used public datasets, and the rest used private data. Our findings suggest that ultrasound-based AI studies for the detection of COVID-19 have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

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COVID-Net US: A Tailored, Highly Efficient, Self-attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-Care Ultrasound Imaging

Cited by 7 publications

References 12 publications

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning

COVIDx-US: An Open-Access Benchmark Dataset of Ultrasound Imaging Data for AI-Driven COVID-19 Analytics

Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies

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