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ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL
OBJECTIVES: Lung ultrasound with an artificial intelligence (AI) application provides a low-cost, non-invasive diagnostic that can play a supporting role in diagnosing COVID-19, especially in areas without PCR/CT access. [1][2] Especially throughout the COVID-19 pandemic fast, safe and highly sensit...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212741/ http://dx.doi.org/10.1016/j.ultrasmedbio.2022.04.024 |
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author | Buhre, Charlotte Born, Jannis Wiedemann, Nina Cossio, Manuel Brändle, Gabriel Leidermann, Konstantin Aujayeb, Avinash Rieck, Bastian Bogwardt, Karsten |
author_facet | Buhre, Charlotte Born, Jannis Wiedemann, Nina Cossio, Manuel Brändle, Gabriel Leidermann, Konstantin Aujayeb, Avinash Rieck, Bastian Bogwardt, Karsten |
author_sort | Buhre, Charlotte |
collection | PubMed |
description | OBJECTIVES: Lung ultrasound with an artificial intelligence (AI) application provides a low-cost, non-invasive diagnostic that can play a supporting role in diagnosing COVID-19, especially in areas without PCR/CT access. [1][2] Especially throughout the COVID-19 pandemic fast, safe and highly sensitive diagnostic tools are crucial. [3] The goal of this work was twofold: 1. create a publicly available dataset of lung ultrasound images/videos and 2. train an AI algorithm to detect and classify COVID-19 on lung ultrasound images and videos. MATERIALS: The largest publicly available COVID-19 lung ultrasound dataset was created from a variety of sources, with > 200 videos and > 50 images. The dataset is heterogeneous, mostly acquired with a convex transducer and according to BLUE protocol. Using available additional patient information, lung ultrasound images in the dataset were categorized as COVID-19, bacterial pneumonia, other viral pneumonia, and healthy. In addition, two independent reviewers evaluated the visible pathologies in the lung ultrasound images. On the dataset, an in-depth study of deep learning methods for differential diagnosis of lung pathologies was performed. RESULTS: In the COVID-19 ultrasound images and videos lung ultrasound signs of a nonspecific pneuomia (fragmented pleural lines, B-lines, (subpleural) consolidations, aero bronchograms and pleural effusions) were visible.The frame-based model correctly distinguished COVID-19 lung ultrasound images from healthy and bacterial pneumonia with a sensitivity of 0.90 ± 0.08 and a specificity of 0.96 ± 0.04. CONCLUSIONS: Our work shows promising results of AI application in the field of lung sonography using COVID-19 as an example. Currently, the AI model is in the clinical trial phase. The data set as well as the code for the CNN are publicly available: https://github.com/BorgwardtLab/covid19_ultrasound. The provided dataset facilitates the validation of lung ultrasound based neural networks to develop fast, accessible screening methods for pulmonary diseases. |
format | Online Article Text |
id | pubmed-9212741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92127412022-06-22 ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL Buhre, Charlotte Born, Jannis Wiedemann, Nina Cossio, Manuel Brändle, Gabriel Leidermann, Konstantin Aujayeb, Avinash Rieck, Bastian Bogwardt, Karsten Ultrasound Med Biol Op 02 OBJECTIVES: Lung ultrasound with an artificial intelligence (AI) application provides a low-cost, non-invasive diagnostic that can play a supporting role in diagnosing COVID-19, especially in areas without PCR/CT access. [1][2] Especially throughout the COVID-19 pandemic fast, safe and highly sensitive diagnostic tools are crucial. [3] The goal of this work was twofold: 1. create a publicly available dataset of lung ultrasound images/videos and 2. train an AI algorithm to detect and classify COVID-19 on lung ultrasound images and videos. MATERIALS: The largest publicly available COVID-19 lung ultrasound dataset was created from a variety of sources, with > 200 videos and > 50 images. The dataset is heterogeneous, mostly acquired with a convex transducer and according to BLUE protocol. Using available additional patient information, lung ultrasound images in the dataset were categorized as COVID-19, bacterial pneumonia, other viral pneumonia, and healthy. In addition, two independent reviewers evaluated the visible pathologies in the lung ultrasound images. On the dataset, an in-depth study of deep learning methods for differential diagnosis of lung pathologies was performed. RESULTS: In the COVID-19 ultrasound images and videos lung ultrasound signs of a nonspecific pneuomia (fragmented pleural lines, B-lines, (subpleural) consolidations, aero bronchograms and pleural effusions) were visible.The frame-based model correctly distinguished COVID-19 lung ultrasound images from healthy and bacterial pneumonia with a sensitivity of 0.90 ± 0.08 and a specificity of 0.96 ± 0.04. CONCLUSIONS: Our work shows promising results of AI application in the field of lung sonography using COVID-19 as an example. Currently, the AI model is in the clinical trial phase. The data set as well as the code for the CNN are publicly available: https://github.com/BorgwardtLab/covid19_ultrasound. The provided dataset facilitates the validation of lung ultrasound based neural networks to develop fast, accessible screening methods for pulmonary diseases. Published by Elsevier Inc. 2022 2022-06-20 /pmc/articles/PMC9212741/ http://dx.doi.org/10.1016/j.ultrasmedbio.2022.04.024 Text en Copyright © 2022 Published by Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Op 02 Buhre, Charlotte Born, Jannis Wiedemann, Nina Cossio, Manuel Brändle, Gabriel Leidermann, Konstantin Aujayeb, Avinash Rieck, Bastian Bogwardt, Karsten ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL |
title | ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL |
title_full | ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL |
title_fullStr | ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL |
title_full_unstemmed | ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL |
title_short | ACCELERATING COVID-19 DIFFERENTIAL DIAGNOSISWITH EXPLAINABLE ULTRASOUND IMAGE ANALYSIS: AN AI TOOL |
title_sort | accelerating covid-19 differential diagnosiswith explainable ultrasound image analysis: an ai tool |
topic | Op 02 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9212741/ http://dx.doi.org/10.1016/j.ultrasmedbio.2022.04.024 |
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