COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning Article Swipe
Isah Salim Ahmad
,
Na Li
,
Tangsheng Wang
,
Xuan Liu
,
Jingjing Dai
,
Yinping Chan
,
Haoyang Liu
,
Junming Zhu
,
Weibin Kong
,
Zefeng Lu
,
Yaoqin Xie
,
Xiaokun Liang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering10111314
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering10111314
The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956–0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Receiver operating characteristic
Artificial intelligence
Nuclear medicine
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Deep learning
Medicine
Artificial neural network
Rendering (computer graphics)
Computer science
Medical physics
Machine learning
Internal medicine
Disease
Infectious disease (medical specialty)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering10111314
- https://www.mdpi.com/2306-5354/10/11/1314/pdf?version=1699969440
- OA Status
- gold
- Cited By
- 2
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388653201
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388653201Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bioengineering10111314Digital Object Identifier
- Title
-
COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-14Full publication date if available
- Authors
-
Isah Salim Ahmad, Na Li, Tangsheng Wang, Xuan Liu, Jingjing Dai, Yinping Chan, Haoyang Liu, Junming Zhu, Weibin Kong, Zefeng Lu, Yaoqin Xie, Xiaokun LiangList of authors in order
- Landing page
-
https://doi.org/10.3390/bioengineering10111314Publisher landing page
- PDF URL
-
https://www.mdpi.com/2306-5354/10/11/1314/pdf?version=1699969440Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2306-5354/10/11/1314/pdf?version=1699969440Direct OA link when available
- Concepts
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Coronavirus disease 2019 (COVID-19), Receiver operating characteristic, Artificial intelligence, Nuclear medicine, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Deep learning, Medicine, Artificial neural network, Rendering (computer graphics), Computer science, Medical physics, Machine learning, Internal medicine, Disease, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.infections | 72 |
| abstract_inverted_index.prediction | 197 |
| abstract_inverted_index.presented. | 78 |
| abstract_inverted_index.radiation, | 35 |
| abstract_inverted_index.technology | 43 |
| abstract_inverted_index.Coronavirus | 3 |
| abstract_inverted_index.alternative | 224 |
| abstract_inverted_index.controlling | 10 |
| abstract_inverted_index.effectively | 213 |
| abstract_inverted_index.multicenter | 85 |
| abstract_inverted_index.performance | 189 |
| abstract_inverted_index.specificity | 175 |
| abstract_inverted_index.adaptability | 233 |
| abstract_inverted_index.conventional | 28, 192 |
| abstract_inverted_index.demonstrated | 164, 187 |
| abstract_inverted_index.examinations | 38 |
| abstract_inverted_index.impractical. | 39 |
| abstract_inverted_index.intelligence | 23 |
| abstract_inverted_index.specifically | 65 |
| abstract_inverted_index.specificity, | 152 |
| abstract_inverted_index.ULTRA-X-COVID | 185, 210 |
| abstract_inverted_index.approximately | 94 |
| abstract_inverted_index.effectiveness | 132 |
| abstract_inverted_index.multinational | 83 |
| abstract_inverted_index.retrospective | 57 |
| abstract_inverted_index.ULTRA-X-COVID, | 60 |
| abstract_inverted_index.Ultra-low-dose | 40 |
| abstract_inverted_index.characteristic | 146 |
| abstract_inverted_index.ultra-low-dose | 74, 218 |
| abstract_inverted_index.0.956–0.983), | 171 |
| abstract_inverted_index.characteristic, | 150 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.70066829 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |