Cargando…
Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study
BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7210135/ https://www.ncbi.nlm.nih.gov/pubmed/32395494 http://dx.doi.org/10.21037/atm.2020.03.132 |
_version_ | 1783531218571100160 |
---|---|
author | Yang, Shuyi Jiang, Longquan Cao, Zhuoqun Wang, Liya Cao, Jiawang Feng, Rui Zhang, Zhiyong Xue, Xiangyang Shi, Yuxin Shan, Fei |
author_facet | Yang, Shuyi Jiang, Longquan Cao, Zhuoqun Wang, Liya Cao, Jiawang Feng, Rui Zhang, Zhiyong Xue, Xiangyang Shi, Yuxin Shan, Fei |
author_sort | Yang, Shuyi |
collection | PubMed |
description | BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958–1.0) in the validation set and 0.98 (95% CI: 0.972–0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists’ evaluation) and radiologists’ workload. |
format | Online Article Text |
id | pubmed-7210135 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-72101352020-05-11 Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study Yang, Shuyi Jiang, Longquan Cao, Zhuoqun Wang, Liya Cao, Jiawang Feng, Rui Zhang, Zhiyong Xue, Xiangyang Shi, Yuxin Shan, Fei Ann Transl Med Original Article BACKGROUND: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). METHODS: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. RESULTS: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958–1.0) in the validation set and 0.98 (95% CI: 0.972–0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. CONCLUSIONS: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists’ evaluation) and radiologists’ workload. AME Publishing Company 2020-04 /pmc/articles/PMC7210135/ /pubmed/32395494 http://dx.doi.org/10.21037/atm.2020.03.132 Text en 2020 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Yang, Shuyi Jiang, Longquan Cao, Zhuoqun Wang, Liya Cao, Jiawang Feng, Rui Zhang, Zhiyong Xue, Xiangyang Shi, Yuxin Shan, Fei Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study |
title | Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study |
title_full | Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study |
title_fullStr | Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study |
title_full_unstemmed | Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study |
title_short | Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study |
title_sort | deep learning for detecting corona virus disease 2019 (covid-19) on high-resolution computed tomography: a pilot study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7210135/ https://www.ncbi.nlm.nih.gov/pubmed/32395494 http://dx.doi.org/10.21037/atm.2020.03.132 |
work_keys_str_mv | AT yangshuyi deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT jianglongquan deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT caozhuoqun deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT wangliya deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT caojiawang deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT fengrui deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT zhangzhiyong deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT xuexiangyang deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT shiyuxin deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy AT shanfei deeplearningfordetectingcoronavirusdisease2019covid19onhighresolutioncomputedtomographyapilotstudy |