Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs

Quantitative Imaging in Medicine and Surgery

article

Ngo Fung Daniel Lam¹  Hongfei Sun¹  Liming Song¹  Dongrong Yang¹  Shaohua Zhi¹  Ge Ren¹  Pak Hei Chou¹  Shiu Bun Nelson Wan²  Man Fung Esther Wong²  King Kwong Chan³  Hoi Ching Hailey Tsang³  Feng-Ming (Spring) Kong⁴  Yì Xiáng J. Wáng⁵  Jing Qin⁶  Lawrence Wing Chi Chan¹  Michael Ying¹  Jing Cai¹ 

[1] Department of Health Technology and Informatics, The Hong Kong Polytechnic University;Department of Radiology, Pamela Youde Nethersole Eastern Hospital;Department of Radiology and Imaging, Queen Elizabeth Hospital;Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong;Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong;School of Nursing, The Hong Kong Polytechnic University

关键词: Classification;    bone suppression;    deep learning;    chest radiography;    coronavirus disease 2019 (COVID-19);   

DOI  :  10.21037/qims-21-791

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Results: Bone suppression of external test data was found to significantly (P0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

【 授权许可】

All Rights reserved