Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study

被引：0

作者：

Hiremath A. ^{[1
,2
]}

Viswanathan V.S. ^{[3
]}

Bera K. ^{[4
]}

Shiradkar R. ^{[3
]}

Yuan L. ^{[5
]}

Armitage K. ^{[6
]}

Gilkeson R. ^{[4
]}

Ji M. ^{[7
]}

Fu P. ^{[8
]}

Gupta A. ^{[4
]}

Lu C. ^{[9
,10
,11
]}

Madabhushi A. ^{[12
,13
]}

机构：

[1] Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH

[2] Picture Health, Cleveland, OH

[3] Emory University, Department of Biomedical Engineering, GA

[4] University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH

[5] Renmin Hospital of Wuhan University, Department of Information Center, Hubei, Wuhan

[6] University Hospitals Cleveland Medical Center, Department of Infectious Diseases, Cleveland, OH

[7] Renmin Hospital of Wuhan University, Department of Gastroenterology, Hubei, Wuhan

[8] Case Western Reserve University, Department of Population and Quantitative Health Sciences, Cleveland, OH

[9] Guangdong Provincial People's Hospital, Department of Radiology, Guangdong Academy of Medical Sciences, Guangzhou

[10] Guangdong Provincial People's Hospital, Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Academy of Medical Sciences, Guangzhou

[11] Guangdong Provincial People's Hospital, Medical Research Center, Guangdong Academy of Medical Sciences

[12] Georgia Institute of Technology and Emory University, Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, GA

[13] Atlanta Veterans Administration Medical Center, GA

来源：

Computers in Biology and Medicine | 2024年 / 177卷

关键词：

Compendex;

D O I：

10.1016/j.compbiomed.2024.108643

中图分类号：

学科分类号：

摘要：

Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity. © 2024

引用

共 53 条

[31] Paszke A., Gross S., Massa F., Lerer A., Bradbury J., Chanan G., Killeen T., Lin Z., Gimelshein N., Antiga L., Desmaison A., Kopf A., Yang E., DeVito Z., Raison M., Tejani A., Chilamkurthy S., Steiner B., Fang L., Bai J., Chintala S., PyTorch: an imperative style, high-performance deep learning library, (2019)
[32] Isensee F., Kickingereder P., Wick W., Bendszus M., Maier-Hein K.H., Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, pp. 287-297, (2017)
[33] van Griethuysen J.J.M., Fedorov A., Parmar C., Hosny A., Aucoin N., Narayan V., Beets-Tan R.G.H., Fillion-Robin J.-C., Pieper S., Aerts H.J.W.L., Computational radiomics system to decode the radiographic phenotype, Cancer Res., 77, pp. e104-e107, (2017)
[34] DeLong E.R., DeLong D.M., Clarke-Pearson D.L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, pp. 837-845, (1988)
[35] 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, pp. 336-359, (2020)
[36] Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N., An image is worth 16x16 words: transformers for image recognition at scale, (2021)
[37] Consortium T.M., Project MONAI, (2020)
[38] Osanlouy M., Clark A.R., Kumar H., King C., Wilsher M.L., Milne D.G., Whyte K., Hoffman E.A., Tawhai M.H., Lung and fissure shape is associated with age in healthy never-smoking adults aged 20–90 years, Sci. Rep., 10, (2020)
[39] Luger A.K., Sonnweber T., Gruber L., Schwabl C., Cima K., Tymoszuk P., Gerstner A.K., Pizzini A., Sahanic S., Boehm A., Coen M., Strolz C.J., Woll E., Weiss G., Kirchmair R., Feuchtner G.M., Prosch H., Tancevski I., Loffler-Ragg J., Widmann G., Chest CT of lung injury 1 Year after COVID-19 pneumonia: the CovILD study, Radiology, (2022)
[40] Dimbath E., Maddipati V., Stahl J., Sewell K., Domire Z., George S., Vahdati A., Implications of microscale lung damage for COVID-19 pulmonary ventilation dynamics: a narrative review, Life Sci., 274, (2021)

← 1 2 3 4 5 6 →