Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos

Xinxin Zhi; Jin Li; Junxiang Chen; Lei Wang; Fangfang Xie; Wenrui Dai; Jiayuan Sun; Hongkai Xiong

doi:10.3389/fonc.2021.673775

Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos

Front Oncol. 2021 May 31:11:673775. doi: 10.3389/fonc.2021.673775. eCollection 2021.

Authors

Xinxin Zhi^{1

2

3}, Jin Li⁴, Junxiang Chen^{1

2

3}, Lei Wang⁵, Fangfang Xie^{1

2

3}, Wenrui Dai⁴, Jiayuan Sun^{1

2

3}, Hongkai Xiong⁴

Affiliations

¹ Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
² Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
³ Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
⁴ School of Electronic Information & Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
⁵ Department of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.

Abstract

Background: Endoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.

Methods: LNs with qualified strain elastography videos from June 2019 to November 2019 were enrolled in the training and validation sets randomly at a quantity ratio of 3:1 to train an automatic image selection model using machine learning algorithm. The strain elastography videos in December 2019 were used as the test set, from which three representative images were selected for each LN by the model. Meanwhile, three experts and three trainees selected one representative image severally for each LN on the test set. Qualitative grading score and four quantitative methods were used to evaluate images above to assess the performance of the automatic image selection model.

Results: A total of 415 LNs were included in the training and validation sets and 91 LNs in the test set. Result of the qualitative grading score showed that there was no statistical difference between the three images selected by the machine learning model. Coefficient of variation (CV) values of the four quantitative methods in the machine learning group were all lower than the corresponding CV values in the expert and trainee groups, which demonstrated great stability of the machine learning model. Diagnostic performance analysis on the four quantitative methods showed that the diagnostic accuracies were range from 70.33% to 73.63% in the trainee group, 78.02% to 83.52% in the machine learning group, and 80.22% to 82.42% in the expert group. Moreover, there were no statistical differences in corresponding mean values of the four quantitative methods between the machine learning and expert groups (p >0.05).

Conclusion: The automatic image selection model established in this study can help select stable and high-quality representative images from EBUS strain elastography videos, which has great potential in the diagnosis of intrathoracic LNs.

Keywords: endobronchial ultrasound; image selection; lymph nodes; machine learning; strain elastography.