Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

Wenyu Xing; Chao He; Jiawei Li; Wei Qin; Minglei Yang; Guannan Li; Qingli Li; Dean Ta; Gaofeng Wei; Wenfang Li; Jiangang Chen

doi:10.1016/j.bspc.2022.103561

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

Biomed Signal Process Control. 2022 May:75:103561. doi: 10.1016/j.bspc.2022.103561. Epub 2022 Feb 7.

Authors

Wenyu Xing^{1

2}, Chao He³, Jiawei Li^{4

5}, Wei Qin⁶, Minglei Yang⁷, Guannan Li⁸, Qingli Li⁸, Dean Ta^{1

2

9}, Gaofeng Wei¹⁰, Wenfang Li³, Jiangang Chen^{6

8}

Affiliations

¹ Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China.
² Human Phenome Institute, Fudan University, Shanghai 201203, China.
³ Department of Emergency and Critical Care, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
⁴ Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
⁵ Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
⁶ Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai 201203, China.
⁷ Artificial Intelligence and Clinical Innovation Research, Neusoft Medical Systems Co., Ltd, Shenyang 110167, China.
⁸ Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai 200241, China.
⁹ Academy for Engineering & Technology, Fudan University, Shanghai 200433, China.
¹⁰ Naval Medical Department, Naval Medical University, Shanghai, 200433, China.

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.