Abstract
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultrasound probe. Masking the adipose tissue during training and inference (while retaining the pleural line and Merlin’s space artifacts such as A-lines and B-lines) improved the AI model’s diagnostic accuracy.
G. R. Gare and W. Chen—Equal contribution.
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Simple, Safe, Same: Lung Ultrasound for COVID-19 - Tabular View - ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/record/NCT04322487?term=ultrasound+covid&draw=2&view=record
Born, J., et al.: Accelerating detection of lung pathologies with explainable ultrasound image analysis. Appl. Sci. (Switzerland) 11(2) (2021). https://doi.org/10.3390/app11020672. https://www.mdpi.com/2076-3417/11/2/672
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT’2010, pp. 177–186. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Carrer, L., et al.: Automatic pleural line extraction and COVID-19 scoring from lung ultrasound data. IEEE Trans. Ultrasonics Ferroelectrics Freq. Control 67(11), 2207–2217 (2020). https://doi.org/10.1109/TUFFC.2020.3005512
Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006). https://doi.org/10.1016/j.patrec.2005.10.010
Gao, Y.D., et al.: Risk factors for severe and critically ill COVID-19 patients: a review. Allergy 76(2), 428–455 (2021). https://doi.org/10.1111/ALL.14657. https://onlinelibrary.wiley.com/doi/full/10.1111/all.14657
Gare, G.R., et al.: Dense pixel-labeling for reverse-transfer and diagnostic learning on lung ultrasound for COVID-19 and pneumonia detection. In: Proceedings - International Symposium on Biomedical Imaging 2021-April, pp. 1406–1410 (2021). https://doi.org/10.1109/ISBI48211.2021.9433826
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 770–778. IEEE Computer Society (2016). https://doi.org/10.1109/CVPR.2016.90. http://image-net.org/challenges/LSVRC/2015/
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. International Conference on Learning Representations, ICLR (2015). https://arxiv.org/abs/1412.6980v9
Liang, H.Y., et al.: Ultrasound in neonatal lung disease. Quan. Imaging Med, Surg. 8(5), 535–546 (2018). https://doi.org/10.21037/qims.2018.06.01. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6037955/
Lichtenstein, D.: Novel approaches to ultrasonography of the lung and pleural space: where are we now? (2017). https://doi.org/10.1183/20734735.004717. https://pubmed.ncbi.nlm.nih.gov/28620429/
Lin, J., Gan, C., Han, S.: TSM: temporal shift module for efficient video understanding. In: Proceedings of the IEEE International Conference on Computer Vision 2019-Octob, pp. 7082–7092 (2018). http://arxiv.org/abs/1811.08383
Miller, A.: Practical approach to lung ultrasound. BJA Educ. 16(2), 39–45 (2016). https://doi.org/10.1093/BJACEACCP/MKV012. https://academic.oup.com/bjaed/article/16/2/39/2897763
Ni, W., et al.: Role of angiotensin-converting enzyme 2 (ACE2) in COVID-19. Critical Care 2020 24(1), 1–10 (2020). https://doi.org/10.1186/S13054-020-03120-0. https://ccforum.biomedcentral.com/articles/10.1186/s13054-020-03120-0
Rahman, M.M., Davis, D.N.: Addressing the class imbalance problem in medical datasets. Int. J. Mach. Learn. Comput. 224–228 (2013). https://doi.org/10.7763/ijmlc.2013.v3.307
Roy, S., et al.: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging 39(8), 2676–2687 (2020). https://doi.org/10.1109/TMI.2020.2994459
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336–359 (2016). https://doi.org/10.1007/s11263-019-01228-7
Soldati, G., et al.: Is there a role for lung ultrasound during the COVID-19 pandemic? (2020). https://doi.org/10.1002/jum.15284. www.aium.org
Soldati, G., et al.: Proposal for international standardization of the use of lung ultrasound for patients with COVID-19. J. Ultrasound Med. 39(7), 1413–1419 (2020). https://doi.org/10.1002/jum.15285. https://pubmed.ncbi.nlm.nih.gov/32227492/
Taylor, A., Anjum, F., O’Rourke, M.C.: Thoracic and lung ultrasound. StatPearls (2021). https://www.ncbi.nlm.nih.gov/books/NBK500013/
Xue, W., et al.: Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information. Med. Image Anal. 69, 101975 (2021). https://doi.org/10.1016/j.media.2021.101975
Acknowledgements
This present work was sponsored in part by US Army Medical contract W81XWH-19-C0083. We are pursuing intellectual property protection. Galeotti serves on the advisory board of Activ Surgical, Inc. He and Rodriguez are involved in the startup Elio AI, Inc.
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Gare, G.R. et al. (2021). The Role of Pleura and Adipose in Lung Ultrasound AI. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_14
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