tetano
Editor, Senior Moderator
Biomed Signal Process Control
. 2022 May;75:103561.
doi: 10.1016/j.bspc.2022.103561. Epub 2022 Feb 7.
Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model
Wenyu Xing[SUP] 1 2 [/SUP], Chao He[SUP] 3 [/SUP], Jiawei Li[SUP] 4 5 [/SUP], Wei Qin[SUP] 6 [/SUP], Minglei Yang[SUP] 7 [/SUP], Guannan Li[SUP] 8 [/SUP], Qingli Li[SUP] 8 [/SUP], Dean Ta[SUP] 1 2 9 [/SUP], Gaofeng Wei[SUP] 10 [/SUP], Wenfang Li[SUP] 3 [/SUP], Jiangang Chen[SUP] 6 8 [/SUP]
Affiliations
Abstract
Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.
Keywords: Automated scoring; COVID-19 pneumonia; Cascaded model; Deep learning; LUS.
. 2022 May;75:103561.
doi: 10.1016/j.bspc.2022.103561. Epub 2022 Feb 7.
Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model
Wenyu Xing[SUP] 1 2 [/SUP], Chao He[SUP] 3 [/SUP], Jiawei Li[SUP] 4 5 [/SUP], Wei Qin[SUP] 6 [/SUP], Minglei Yang[SUP] 7 [/SUP], Guannan Li[SUP] 8 [/SUP], Qingli Li[SUP] 8 [/SUP], Dean Ta[SUP] 1 2 9 [/SUP], Gaofeng Wei[SUP] 10 [/SUP], Wenfang Li[SUP] 3 [/SUP], Jiangang Chen[SUP] 6 8 [/SUP]
Affiliations
- PMID: 35154355
- PMCID: PMC8818345
- DOI: 10.1016/j.bspc.2022.103561
Abstract
Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays an important role in the auxiliary diagnosis of COVID-19 pneumonia. However, the lack of medical resources leads to the low using efficiency of the LUS, to address this problem, a novel automated LUS scoring system for evaluating COVID-19 pneumonia based on the two-stage cascaded deep learning model was proposed in this paper. 18,330 LUS images collected from 26 COVID-19 pneumonia patients were successfully assigned scores by two experienced doctors according to the designed four-level scoring standard for training the model. At the first stage, we made a secondary selection of these scored images through five ResNet-50 models and five-fold cross validation to obtain the available 12,949 LUS images which were highly relevant to the initial scoring results. At the second stage, three deep learning models including ResNet-50, Vgg-19, and GoogLeNet were formed the cascaded scored model and trained using the new dataset, whose predictive result was obtained by the voting mechanism. In addition, 1000 LUS images collected another 5 COVID-19 pneumonia patients were employed to test the model. Experiments results showed that the automated LUS scoring model was evaluated in terms of accuracy, sensitivity, specificity, and F1-score, being 96.1%, 96.3%, 98.8%, and 96.1%, respectively. They proved the proposed two-stage cascaded deep learning model could automatically score an LUS image, which has great potential for application to the clinics on various occasions.
Keywords: Automated scoring; COVID-19 pneumonia; Cascaded model; Deep learning; LUS.