[1] 李莉,李宏军.甲型流感肺炎的影像学表现[J].放射学实践, 2014, 29(7): 760-762. DOI: 10.13609/j.cnki.1000-0313.2014.07.007. [2] XIANG X, WANG Z H, YE L L, et al. Co-infection of SARS-COV-2 and influenza A virus: a case series and fast review[J]. Curr Med Sci, 2021, 41(1): 51-57. DOI: 10.1007/s11596-021-2317-2. [3] YUAN Y, TAO X F, SHI Y X, et al. Initial HRCT findings of novel influenza A(H1N1)infection[J]. Influenza Other Respir Viruses, 2012, 6(6): e114-e119. DOI: 10.1111/j.1750-2659.2012.00368.x. [4] YU M, ZHU Y B, QU X Y, et al. Differences in clinical characteristics and chest CT findings between severe and critical H1N1 pneumonia[J]. Clin Respir J, 2023, 17(4): 277-285. DOI: 10.1111/crj.13591. [5] 杜娟,范学杰,陈红梅,等.甲流H1N1流感病毒性肺炎临床特征及CT影像学表现分析[J].中华肺部疾病杂志(电子版), 2019, 12(3): 296-300. DOI: 10.3877 /cma.j.issn.1674-6902.2019.03.006. [6] KOO H J, LIM S, CHOE J, et al. Radiographic and CT features of viral pneumonia[J]. Radiographics, 2018, 38(3): 719-739. DOI: 10.1148/rg.2018170048. [7] MAUAD T, HAJJAR L A, CALLEGARI G D, et al. Lung pathology in fatal novel human influenza A(H1N1)infection[J]. Am J Respir Crit Care Med, 2010, 181(1): 72-79. DOI: 10.1164/rccm.200909-1420OC. [8] CHUNG J H, MONTNER S M, ADEGUNSOYE A, et al. CT findings, radiologic-pathologic correlation, and imaging predictors of survival for patients with interstitial pneumonia with autoimmune features[J]. AJR Am J Roentgenol, 2017, 208(6): 1229-1236. DOI: 10.2214/AJR.16.17121. [9] LV Y, YU G D, ZHANG X L, et al. Comparative analysis of elderly hospitalized patients with COVID-19 or influenza A H1N1 virus infections[J]. Int J Infect Dis, 2022, 125: 278-284. DOI: 10.1016/j.ijid.2022.11.008. [10] 赵桂东.30例甲型H1N1流感肺炎的影像学表现[J].临床医学, 2015, 35(6): 101-102. [11] AVIRAM G, BAR-SHAI A, SOSNA J, et al. H1N1 influenza: initial chest radiographic findings in helping predict patient outcome[J]. Radiology, 2010, 255(1): 252-259. DOI: 10.1148/radiol.10092240. [12] TORUN ?瘙塁, KESIM Ç, SÜNER A, et al. Influenza viruses and SARS-CoV-2 in adult: ‘Similarities and differences’[J]. Tuberk Toraks, 2021, 69(4): 458-468. DOI: 10.5578/tt.20219603. [13] 鲍永霞,曹智刚,王晶,等.甲型H1N1流感肺炎30例胸部影像学分析[J].实用医学杂志, 2011, 27(12): 2220-2222. DOI: 10.3969/j.issn.1006-5725.2011.12.049. [14] GAO L, ZHANG J. Pulmonary high-resolution computed tomography(HRCT)findings of patients with early-stage coronavirus disease 2019(COVID-19)in Hangzhou, China[J]. Med Sci Monit, 2020, 26: e923885. DOI: 10.12659/msm.923885. [15] XU Z, SHI L, WANG Y, et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome[J]. Lancet Respir Med, 2020, 8(4): 420-422. DOI: 10.1016/S2213-2600(20)30076-X. [16] MUROTA M, JOHKOH T, LEE K S, et al. Influenza H1N1 virus-associated pneumonia often resembles rapidly progressive interstitial lung disease seen in collagen vascular diseases and COVID-19 pneumonia; CT-pathologic correlation in 24 patients[J]. Eur J Radiol Open, 2020, 7: 100297. DOI: 10.1016/j.ejro.2020.100297. [17] SCHOEN K, HORVAT N, GUERREIRO N F C, et al. Spectrum of clinical and radiographic findings in patients with diagnosis of H1N1 and correlation with clinical severity[J]. BMC Infect Dis, 2019, 19(1): 964. DOI: 10.1186/s12879-019-4592-0. [18] LIN L Y, FU G Z, CHEN S L, et al. CT manifestations of coronavirus disease(COVID-19)pneumonia and influenza virus pneumonia: a comparative study[J]. AJR Am J Roentgenol, 2021, 216(1): 71-79. DOI: 10.2214/AJR.20.23304. [19] YU X D, LIU W H, XIA F, et al. Artificial intelligence-based CT metrics used in predicting clinical outcome of COVID-19 in young and middle-aged adults[J]. Med Phys, 2022, 49(8): 5604-5615. DOI: 10.1002/mp.15803. [20] STEFANIDIS K, KONSTANTELOU E, YUSUF G T, et al. Radiological, epidemiological and clinical patterns of pulmonary viral infections[J]. Eur J Radiol, 2021, 136: 109548. DOI: 10.1016/j.ejrad.2021.109548. [21] KHAN A, AKRAM M U, NAZIR S. Automated grading of chest X-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization[J]. PLoS One, 2023, 18(1): e0280352. DOI: 10.1371/journal.pone.0280352. [22] YANG Z Q, LIN D Y, CHEN X F, et al. Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features[J]. Front Microbiol, 2022, 13: 847836. DOI: 10.3389/fmicb.2022.847836. [23] LIU M, HAN Y L, SUN J, et al. Comparison of the epidemiological and clinical characteristics of hospitalized children with pneumonia caused by SARS-CoV-2, influenza A, and human adenoviruses: a case-control study[J]. Clin Pediatr(Phila), 2022, 61(2): 150-158. DOI: 10.1177/00099228211058601. [24] WAN S, LI M Q, YE Z, et al. CT manifestations and clinical characteristics of 1115 patients with coronavirus disease 2019(COVID-19): a systematic review and meta-analysis[J]. Acad Radiol, 2020, 27(7): 910-921. DOI: 10.1016/j.acra.2020.04.033. [25] YE Z, ZHANG Y, WANG Y, et al. Chest CT manifestations of new coronavirus disease 2019(COVID-19): a pictorial review[J]. Eur Radiol, 2020, 30(8): 4381-4389. DOI: 10.1007/s00330-020-06801-0. [26] HUANG Y L, ZHANG Z G, LIU S Y, et al. CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia[J]. BMC Med Imaging, 2021, 21(1): 31. DOI: 10.1186/s12880-021-00564-w. [27] FISCHER T, EL BAZ Y, SCANFERLA G, et al. Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study[J]. Eur J Radiol Open, 2022, 9: 100431. DOI: 10.1016/j.ejro.2022.100431. [28] WU Z Y, LI L, JIN R H, et al. Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19[J]. Eur J Radiol, 2021, 137: 109602. DOI: 10.1016/j.ejrad.2021.109602. [29] XIAO A L, ZHAO H J, XIA J B, et al. Triage modeling for differential diagnosis between COVID-19 and human influenza A pneumonia: classification and regression tree analysis[J]. Front Med(Lausanne), 2021, 8: 673253. DOI: 10.3389/fmed.2021.673253. [30] LV D F, YING Q M, HE Y W, et al. Differential diagnosis of coronavirus disease 2019 pneumonia or influenza A pneumonia by clinical characteristics and laboratory findings[J]. J Clin Lab Anal, 2021, 35(2): e23685. DOI: 10.1002/jcla.23685. [31] QUAN S, CHEN H, LIN L. Automatic CT whole-lung segmentation in radiomics discrimination: methodology and application in pneumonia diagnosis and distinguishment[J]. Displays, 2022, 71: 102144. DOI: 10.1016/j.displa.2021.102144. [32] VAIDYANATHAN A, GUIOT J, ZERKA F, et al. An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography[J]. ERJ Open Res, 2022, 8(2): 00579-02021. DOI: 10.1183/23120541.00579-2021. [33] ZHOU M, YANG D X, CHEN Y, et al. Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia[J]. Ann Transl Med, 2021, 9(2): 111. DOI: 10.21037/atm-20-5328. [34] LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42: 60-88. DOI: 10.1016/j.media.2017.07.005. [35] AFTAB M, AMIN R, KOUNDAL D, et al. Classification of COVID-19 and influenza patients using deep learning[J]. Contrast Media Mol Imaging, 2022, 2022: 8549707. DOI: 10.1155/2022/8549707. [36] ABDULSALAM HAMWI W, ALMUSTAFA M M. Development and integration of VGG and dense transfer-learning systems supported with diverse lung images for discovery of the Coronavirus identity[J]. Inform Med Unlocked, 2022, 32: 101004. DOI: 10.1016/j.imu.2022.101004. [37] OKIYAMA S, FUKUDA M, SODE M, et al. Examining the use of an artificial intelligence model to diagnose influenza: development and validation study[J]. J Med Internet Res, 2022, 24(12): e38751. DOI: 10.2196/38751. [38] 陈远彬, 何冰, 林琳, 等. 流感双解方治疗轻型流感病毒性肺炎26例临床观察[J]. 中医杂志, 2017, 58(2): 128-132. DOI: 10.13288/j.11-2166/r.2017.02.010. |