AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.5608
Submit to AIH
Cite this article
6
Download
47
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Enhancing COVID-19 severity assessment with artificial intelligence-based bone suppression technique in chest radiography

Asumi Yamazaki1 ,  Masashi Seki2 ,  Takayuki Ishida1*
Show Less
1 Division of Health Sciences, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
2 Department of Radiology, Kitasato University Hospital, Sagamihara, Kanagawa, Japan
AIH, 5608 https://doi.org/10.36922/aih.5608
Submitted: 28 October 2024 | Revised: 11 February 2025 | Accepted: 21 February 2025 | Published: 5 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
Cite
XML
Abstract

Chest radiography (CXR) is widely used for initial respiratory assessment, but its lesion detection capability is typically inferior to that of computed tomography. Several studies have reported that artificial intelligence (AI)-based bone suppression techniques can enhance the accuracy of lesion detection and disease classification. Previously, we developed an AI-based bone suppression system based on dual-energy subtraction principles. However, the subtraction process limited its versatility and introduced significant artifacts. To overcome these challenges, we improved the system to generate bone-suppressed images directly, eliminating the need for subtraction. This study demonstrates the utility of the updated bone suppression system as a pre-processing tool for regression analyses in assessing coronavirus disease 2019 severity. Four regression models – DenseNet, ResNet18, ResNet50, and RegNetY-120 – were employed to predict the severity based on scores annotated by radiologists. Except for DenseNet, all models showed statistically significant improvements in Pearson correlation coefficients (PCCs) when using bone-suppressed images generated by the updated model. The highest PCC, 0.895, was achieved by the ResNet18 model. The direct image generation process improved the clinical practicality of the bone suppression system while reducing artifacts. Furthermore, the significant improvement in linearity suggests that AI-driven bone suppression enhances the visibility of abnormalities and improves the accuracy for pulmonary condition assessments. These advancements could expand the application of bone suppression techniques in various regression analyses, including disease severity, progression, and recurrence risk. Nonetheless, further validation using larger and more diverse datasets, as well as a broader range of prediction models, is necessary.

Keywords
Bone suppression
Artificial intelligence
COVID-19
Regression model
Chest radiography
Funding
This work was supported by the Kondou Kinen Medical Foundation.
Conflict of interest
The authors declare they have no competing interests.
References
  1. World Health Organization. Coronavirus Disease (COVID-19) Pandemic. Available from: https://www.who. int/emergencies/diseases/novel-coronavirus-2019 [Last accessed on 2024 Oct 15].

 

  1. Beeraka NM, Sukocheva OA, Lukina E, Liu J, Fan R. Development of antibody resistance in emerging mutant strains of SARS CoV-2: Impediment for COVID-19 vaccines. Rev Med Virol. 2022;32(5):e2346. doi: 10.1002/rmv.2346

 

  1. Ramirez FE, Sanchez A, Pirskanen A.T. Hydrothermotherapy in prevention and treatment of mild to moderate cases of COVID-19. Med Hypotheses. 2021;146:110363. doi: 10.1016/j.mehy.2020.110363

 

  1. World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Available from: https://data.who. int/dashboards/covid19/deaths?n=c [Last accessed on 2024 Oct 15].

 

  1. Gao Z, Xu Y, Sun C, et al. A systematic review of asymptomatic infections with COVID-19. J Microbiol Immunol Infect. 2021;54(1):12-16. doi: 10.1016/j.jmii.2020.05.001

 

  1. Kim MS, An MH, Kim WJ, Hwang TH. Comparative efficacy and safety of pharmacological interventions for the treatment of COVID-19: A systematic review and network meta-analysis. PLoS Med. 2020;17(12):e1003501. doi: 10.1371/journal.pmed.1003501

 

  1. Long B, Carius BM, Chavez S, et al. Clinical update on COVID-19 for the emergency clinician: Presentation and evaluation. Am J Emerg Med. 2022;54:46-57. doi: 10.1016/j.ajem.2022.01.028

 

  1. Wong HYF, Lam HYS, Fong AH, et al. Frequency and distribution of chest radiographic findings in patients positive for COVID-19. Radiology. 2020;296(2):E72-E78. doi: 10.1148/radiol.2020201160

 

  1. Sverzellati N, Ryerson CJ, Milanese G, et al. Chest radiography or computed tomography for COVID-19 pneumonia? Comparative study in a simulated triage setting. Eur Respir J. 2021;58(3):2004188. doi: 10.1183/13993003.04188-2020

 

  1. Tang JS, Lai JK, McCusker MW, Irving L, Pascoe DM, Heinze SB. Chest imaging findings in COVID-19-positive patients in an Australian tertiary hospital. J Med Imaging Radiat Oncol. 2022;66(6):755-760. doi: 10.1111/1754-9485.13339

 

  1. Landini N, Colzani G, Ciet P, et al. Chest radiography findings of COVID-19 pneumonia: A specific pattern for a confident differential diagnosis. Acta Radiol. 2022;63(12):1619-1626. doi: 10.1177/02841851211055163

 

  1. Sharifrazi D, Alizadehsani R, Roshanzamir M, et al. Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomed Signal Process Control. 2021;68:102622. doi: 10.1016/j.bspc.2021.102622

 

  1. Arias-Garzón D, Alzate-Grisales JA, Orozco-Arias S, et al. COVID-19 detection in X-ray images using convolutional neural networks. Mach Learn Appl. 2021;6:100138. doi: 10.1016/j.mlwa.2021.100138

 

  1. Avolio M, Fuduli A, Vocaturo E, Zumpano E. A comparative study of linear type multiple instance learning techniques for detecting COVID-19 by chest X-ray images. Prog Artif Intell. 2024. doi: 10.1007/s13748-024-00332-1

 

  1. Takaki T, Murakami S, Tani N, Aoki T. Evaluation of the clinical utility of temporal subtraction using bone suppression processing in digital chest radiography. Heliyon. 2023;9(1):e13004. doi: 10.1016/j.heliyon.2023

 

  1. Van der Heyden B. The potential application of dual-energy subtraction radiography for COVID-19 pneumonia imaging. Br J Radiol. 2021;94(1120):20201384. doi: 10.1259/bjr.20201384

 

  1. Matsubara N, Teramoto A, Saito K, Fujita H. Bone suppression for chest X-ray image using a convolutional neural filter. Australas Phys Eng Sci Med. 2019;43:97-108. doi: 10.1007/s13246-019-00822-w

 

  1. Han L, Lyu Y, Peng C, Zhou SK. GAN-based disentanglement learning for chest X-ray rib suppression. Med Image Anal. 2022;77:102369. doi: 10.1016/j.media.2022.102369

 

  1. Cho K, Seo J, Kyung S, Kim M, Hong GS, Kim N. Bone suppression on pediatric chest radiographs via a deep learning-based cascade model. Comput Methods Programs Biomed. 2022;215:106627. doi: 10.1016/j.cmpb.2022.106627

 

  1. Xie W, Gan M, Tan X, Li M, Yang W, Wang W. Efficient labeling for fine-tuning chest X-ray bone-suppression networks for pediatric patients. Med Phys. 2024;52:978-992. doi: 10.1002/mp.17516

 

  1. Ait Nasser A, Akhloufi MA. A review of recent advances in deep learning models for chest disease detection using radiography. Diagnostics (Basel). 2023;13(1):159. doi: 10.3390/diagnostics13010159

 

  1. Yamazaki A, Koshida A, Tanaka T, Seki M, Ishida T. Development of artificial intelligence-based dual-energy subtraction for chest radiography. Appl Sci. 2023;13(12):7220. doi: 10.3390/app13127220

 

  1. Rani G, Misra A, Dhaka VS, Zumpano E, Vocaturo E. Spatial feature and resolution maximization GAN for bone suppression in chest radiographs. Comput Methods Programs Biomed. 2022;224:107024. doi: 10.1016/j.cmpb.2022.107024

 

  1. Rani G, Misra A, Dhaka VS, et al. A multi-modal bone suppression, lung segmentation, and classification approach for accurate COVID-19 detection using chest radiographs. Intell Syst Appl. 2022;16:200148. doi: 10.1016/j.iswa.2022.200148

 

  1. Lam NFD, Sun H, Song L, et al. Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs. Quant Imaging Med Surg. 2022;12(7):3917-3931. doi: 10.21037/qims-21-791

 

  1. Rajaraman S, Zamzmi G, Folio L, Alderson P, Antani S. Chest X-ray bone suppression for improving classification of tuberculosis-consistent findings. Diagnostics (Basel). 2021;11(5):840. doi: 10.3390/diagnostics11050840

 

  1. Rajaraman S, Cohen G, Spear L, Folio L, Antani S. DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs. PLoS One. 2022;17(3):e0265691. doi: 10.1371/journal.pone.0265691

 

  1. Kim H, Lee KH, Han K, et al. Development and validation of a deep learning-based synthetic bone-suppressed model for pulmonary nodule detection in chest radiographs. JAMA Netw Open. 2023;6(1):e2253820. doi: 10.1001/jamanetworkopen.2022.53820

 

  1. Bae K, Oh DY, Yun ID, Jeon KN. Bone suppression on chest radiographs for pulmonary nodule detection: Comparison between a generative adversarial network and dual-energy subtraction. Korean J Radiol. 2022;23(1):139-149. doi: 10.3348/kjr.2021.0146

 

  1. Xu D, Xu Q, Nhieu K, Ruan D, Sheng K. An efficient and robust method for chest X-ray rib suppression that improves pulmonary abnormality diagnosis. Diagnostics (Basel). 2023;13(9):1652. doi: 10.3390/diagnostics13091652

 

  1. Thölke P, Mantilla-Ramos YJ, Abdelhedi H, et al. Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data. Neuroimage. 2023;277:120253. doi: 10.1016/j.neuroimage.2023.120253

 

  1. Cohen JP, Dao L, Roth K, et al. Predicting COVID-19 pneumonia severity on chest X-ray with deep learning. Cureus. 2020;12(7):e9448. doi: 10.7759/cureus.9448

 

  1. Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M. COVID-19 image data collection: Prospective predictions are the future. J Mach Learn Biomed Imaging. 2020;2:1-38. doi: 10.59275/j.melba.2020-48g7

 

  1. Signoroni A, Savardi M, Benini S, et al. BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset. Med Image Anal. 2021;71:102046. doi: 10.1016/j.media.2021.102046

 

  1. Borghesi A, Maroldi R. COVID-19 outbreak in Italy: Experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol Med. 2020;125:509-513. doi: 10.1007/s11547-020-01200-3

 

  1. Borghesi A, Zigliani A, Masciullo R, et al. Radiographic severity index in COVID-19 pneumonia: Relationship to age and sex in 783 Italian patients. Radiol Med. 2020;125:461-464. doi: 10.1007/s11547-020-01202-1

 

  1. Caroprese L, Vocaturo E, Zumpano E. Argumentation approaches for explanaible AI in medical informatics. Intell Syst Appl. 2022;16:200109. doi: 10.1016/j.iswa.2022.200109

 

  1. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. In 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE; 2017. p. 618-626. doi: 10.1109/ICCV.2017.74

 

  1. Musthafa MM, Mahesh TR, Vinoth Kumar V, Guluwadi S. Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with resnet 50. BMC Med Imaging. 2024;24(1):107. doi: 10.1186/s12880-024-01292-7

 

  1. Talaat FM, Gamel SA, El-Balka RM, Shehata M, ZainEldin H. Grad-CAM enabled breast cancer classification with a 3D inception-ResNet V2: Empowering radiologists with explainable insights. Cancers (Basel). 2024;16(21):3668. doi: 10.3390/cancers16213668

 

  1. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention- MICCAI 2015. Cham: Springer; 2015. p. 234-241.

 

  1. JSRT Database. Available from: https://db.jsrt.or.jp/eng.php [Last accessed on 2024 Oct 15].

 

  1. Isora P, Zhu JY, Zhou T, Efros AA. Image-to-image Translation with Conditional Adversarial Networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE; 2017. p. 5967-5976. doi: 10.1109/CVPR.2017.632

 

  1. Junyanz/Pytorch-CycleGAN-and-Pix2pix. Available from: https://github.com/junyanz/pytorch-cyclegan-and-pix2pix [Last accessed on 2024 Oct 15].

 

  1. Horé A, Ziou D. Image Quality Metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition. Istanbul, Turkey: IEEE; 2010. p. 2366-2369. doi: 10.1109/ICPR.2010.579

 

  1. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600-612. doi: 10.1109/tip.2003.819861

 

  1. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE; 2017. p. 2261-2269. doi: 10.1109/CVPR.2017.243

 

  1. He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE; 2016. p. 770-778. doi: 10.1109/CVPR.2016.90

 

  1. Radosavovic I, Kosaraju RP, Girshick R, He K, Dollár P. Designing Network Design Spaces. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2020. p. 10428-10436. doi: 10.48550/arXiv.2003.13678

 

  1. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A Large-scale Hierarchical Image Database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Miami, FL, USA: IEEE; 2009. p. 248-255. doi: 10.1109/CVPR.2009.5206848

 

  1. Schober P, Boer C, Schwarte LA. Correlation coefficients: Appropriate use and interpretation. Anesth Analg. 2018;126(5):1763-1768. doi: 10.1213/ANE.0000000000002864

 

  1. Karim KS, Tilley IS. Portable single-exposure dual-energy X-ray detector for improved point-of-care diagnostic imaging. Mil Med. 2023;188(Suppl 6):84-91. doi: 10.1093/milmed/usad034

 

  1. Shunkov YE, Kobylkin IS, Prokhorov AV, et al. Motion artefact reduction in dual-energy radiography. Biomed Eng. 2022;55:415-419. doi: 10.1007/s10527-022-10148-9

 

  1. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A. Improved Training of Wasserstein GANs. In: Neural Information Processing Systems 30 (NIPS 2017). San Diego, CA, USA; 2017. p. 5769-5779.

 

  1. Zhang L, Xu Z, Barnes C, et al. Perceptual Artifacts Localization for Image Synthesis Tasks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Paris, France: IEEE; 2023. p. 7545-7556. doi: 10.1109/ICCV51070.2023.00697

 

Share
Back to top
Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept