Artificial intelligence to evaluate diagnosed COVID-19 chest radiographs
DOI:
https://doi.org/10.15392/bjrs.v10i3.2056Keywords:
x-ray, Artificial inteligence, RadiographyAbstract
We present a Machine Learning algorithm based on Python which can be used to aid COVID-19 diagnosis. This algorithm employs Convolutional Neural Networks (CNN) of ResNet-18 architecture from thoracic X-ray images to build a trained dataset that enables further comparisons between common pulmonary diseases and COVID-19 diagnosed patients to classify the radiological findings as being due the COVID-19 or other pathologies. We discuss the importance of setting the right parameters related to training and what they might represent in clinical procedures. We used a dataset containing 942 COVID-19 labeled radiographs from HCPA - Hospital das Clínicas de Porto Alegre and compared it to a public dataset from NIH Clinical Center containing images of pulmonary diseases. Lastly, our trained model had an accuracy of 81.76% for the imbalanced classes and an accuracy of 46.94% for the balanced classes, when compared to other pulmonary diseases such as pneumonia, edema, mass, consolidation, and fibrosis. These results disclose the difficulty of diagnosing COVID-19 from a chest radiograph as it resembles other pulmonary illnesses and makes room for further research in this matter.
- Views: 316
- PDF Downloads: 244
- XML Downloads: 18
Downloads
References
ELKINS A.; FREITAS F. F.; SANZ V. Developing an app to interpret chest X-rays to support the diagnosis of respiratory pathology with artificial intelligence. In: J Med Artif Intell, 2020. DOI: https://doi.org/10.21037/jmai.2019.12.01
CASCELLA M.; RAJNIK M.; ALEEM A. et al. Features, Evaluation, and Treatment of Coronavirus (COVID-19) [Updated 2021 Apr 20]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2021. Available at: <https://www.ncbi.nlm.nih.gov/books/NBK554776/>. Last acessed 12 June 2021
WHO. WHO Coronavirus (COVID-19) Dashboard. Available at: <https://covid19.who.int>. Last acecessed 10 February 2022.
WANG D.; HU B.; HU, C.; ZHU, F.; LIU, X.; ZHANG, J. et al. Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus Infected Pneumonia in Wuhan, China. In: JAMA, 2020. DOI: https://doi.org/10.1001/jama.2020.1585
HARMON S. A. ; SANFORD T. H. ; XU S. et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. In: Nat Commun 11, 4080, 2020. DOI: https://doi.org/10.1038/s41467-020-17971-2
WU J. ; WONG K. ; GUR Y. et al. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents. In: JAMA Netw Open, 2020. DOI: https://doi.org/10.1001/jamanetworkopen.2020.22779
ELLAHHAM S. Artificial intelligence in the diagnosis and management of COVID-19: a narrative review. In: J Med Artif Intell, 2021. DOI: https://doi.org/10.21037/jmai-20-48
ZHU J.; SHEN B. ; ABBASI A. ; HOSHMAND-KOCHI M. ; LI H. ; DUONG T. Q. Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. In: PLoS ONE, 2020. DOI: https://doi.org/10.1371/journal.pone.0236621
WEHBE R. M. ; SHENG J. ; DUTTA S. ; CHAI S. ; DRAVID A. ; BARUTCU S. ; WU Y. ; CANTRELL D. R. ; XIAO N. ; ALLEN B. D. ; MACNEALY G. A. ; SAVAS H. ; AGRAWAL R. ; PAREKH N. ; KATSAGGELOS A. K. DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set. In: Radiology, vol. 299:1, pp. E167-E176, 2021. DOI: https://doi.org/10.1148/radiol.2020203511
LOPEZ-CABRERA J. D. ; OROZCO-MORALES R. ; PORTAL-DIAZ J. A. et al. Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. In: Health Technol. vol. 11, pp. 411–424, 2021. DOI: https://doi.org/10.1007/s12553-021-00520-2
JIAO Z. ; CHOI J. W. ; HALSEY K. ; TRAN T. M. L. ; HSIEH B. ; WANG D. ; EWEJE F.; WANG R. ; CHANG K. ; WU J. ; COLLINS S. A.; YI T. Y. ; DELWORTH A. T. ; LIU T. ; HEALEY T. T.; LU S.; WANG J. ; FENG X.; ATALAY M. K. et al. Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. In: The Lancet Digital Health, vol. 3 pp. E286-E294, 2021. DOI: https://doi.org/10.1016/S2589-7500(21)00039-X
SUMMERS R. M. Artificial Intelligence of COVID-19 Imaging: A Hammer in Search of a Nail. In: Radiology, vol. 298(3), pp. E162-E164, 2021. DOI: https://doi.org/10.1148/radiol.2020204226
GitHub code repository. Available at :<https://github.com/BYTakara/covid_model>. Last accessed 8 February 2022.
NIHCC - CXR8 Data Set. Available at:<https://nihcc.app.box.com/v/ChestXray-NIHCC>. Last accessed 20 June 2021
ABELAIRA M. D. C.; ABELAIRA F. C. ; RUANO-RAVINA A. ; FERNANDEZ-VILLAR A. Use of Conventional Chest Imaging and Artificial Intelligence in COVID-19 Infection. A Review of the Literature. In: Open Respiratory Archives, vol. 3, 2021. DOI: https://doi.org/10.1016/j.opresp.2020.100078
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Brazilian Journal of Radiation Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing: The BJRS articles are licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
