Ultrasound-Based AI for COVID-19 Detection: A Comprehensive Review of Public and Private Lung Ultrasound Datasets and Studies
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 for detecting COVID-19 lung infections. Among various...
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Main Authors: | , , , , , , , , , , , , , , , , , , |
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Format: | Journal Article |
Language: | English |
Published: |
06-11-2024
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Subjects: | |
Online Access: | Get full text |
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Summary: | 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 for detecting COVID-19 lung infections. Among various
medical imaging modalities, ultrasound stands out as a low-cost, mobile, and
radiation-safe imaging technology. In this comprehensive review, we focus on
AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and
analysis. We provide a detailed overview of both publicly available and private
LUS datasets and categorize the AI studies according to the dataset they used.
Additionally, we systematically analyzed and tabulated the studies across
various dimensions, including data preprocessing methods, AI models,
cross-validation techniques, and evaluation metrics. In total, we reviewed 60
articles, 41 of which utilized public datasets, while the remaining employed
private data. Our findings suggest that ultrasound-based AI studies for
COVID-19 detection 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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DOI: | 10.48550/arxiv.2411.05029 |