BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Chao H, Fang X, Zhang J, Homayounieh F, Arru CD, Digumarthy SR, Babaei R, Mobin HK, Mohseni I, Saba L, Carriero A, Falaschi Z, Pasche A, Wang G, Kalra MK, Yan P. Integrative analysis for COVID-19 patient outcome prediction. Med Image Anal 2021;67:101844. [PMID: 33091743 DOI: 10.1016/j.media.2020.101844] [Cited by in Crossref: 19] [Cited by in F6Publishing: 13] [Article Influence: 9.5] [Reference Citation Analysis]
Number Citing Articles
1 Mader C, Bernatz S, Michalik S, Koch V, Martin SS, Mahmoudi S, Basten L, Grünewald LD, Bucher A, Albrecht MH, Vogl TJ, Booz C. Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients. Acad Radiol 2021;28:1048-57. [PMID: 33741210 DOI: 10.1016/j.acra.2021.03.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Giorgetti A, Orazietti V, Busardò FP, Pirani F, Giorgetti R. Died with or Died of? Development and Testing of a SARS CoV-2 Significance Score to Assess the Role of COVID-19 in the Deaths of Affected Patients. Diagnostics (Basel) 2021;11:190. [PMID: 33525705 DOI: 10.3390/diagnostics11020190] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Wang D, Huang C, Bao S, Fan T, Sun Z, Wang Y, Jiang H, Wang S. Study on the prognosis predictive model of COVID-19 patients based on CT radiomics. Sci Rep 2021;11:11591. [PMID: 34078950 DOI: 10.1038/s41598-021-90991-0] [Reference Citation Analysis]
4 Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021;11:886. [PMID: 34575663 DOI: 10.3390/jpm11090886] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Tariq A, Celi LA, Newsome JM, Purkayastha S, Bhatia NK, Trivedi H, Gichoya JW, Banerjee I. Patient-specific COVID-19 resource utilization prediction using fusion AI model. NPJ Digit Med 2021;4:94. [PMID: 34083734 DOI: 10.1038/s41746-021-00461-0] [Reference Citation Analysis]
6 Fang X, Kruger U, Homayounieh F, Chao H, Zhang J, Digumarthy SR, Arru CD, Kalra MK, Yan P. Association of AI quantified COVID-19 chest CT and patient outcome. Int J Comput Assist Radiol Surg 2021;16:435-45. [PMID: 33484428 DOI: 10.1007/s11548-020-02299-5] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
7 Chieregato M, Frangiamore F, Morassi M, Baresi C, Nici S, Bassetti C, Bnà C, Galelli M. A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data. Sci Rep 2022;12. [DOI: 10.1038/s41598-022-07890-1] [Reference Citation Analysis]
8 Chatzitofis A, Cancian P, Gkitsas V, Carlucci A, Stalidis P, Albanis G, Karakottas A, Semertzidis T, Daras P, Giannitto C, Casiraghi E, Sposta FM, Vatteroni G, Ammirabile A, Lofino L, Ragucci P, Laino ME, Voza A, Desai A, Cecconi M, Balzarini L, Chiti A, Zarpalas D, Savevski V. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment. Int J Environ Res Public Health 2021;18:2842. [PMID: 33799509 DOI: 10.3390/ijerph18062842] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
9 AlJame M, Imtiaz A, Ahmad I, Mohammed A. Deep forest model for diagnosing COVID-19 from routine blood tests. Sci Rep 2021;11:16682. [PMID: 34404838 DOI: 10.1038/s41598-021-95957-w] [Reference Citation Analysis]
10 Chen HJ, Mao L, Chen Y, Yuan L, Wang F, Li X, Cai Q, Qiu J, Chen F. Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia. BMC Infect Dis 2021;21:931. [PMID: 34496794 DOI: 10.1186/s12879-021-06614-6] [Reference Citation Analysis]
11 Li Z, Zhao W, Shi F, Qi L, Xie X, Wei Y, Ding Z, Gao Y, Wu S, Liu J, Shi Y, Shen D. A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning. Med Image Anal 2021;69:101978. [PMID: 33588121 DOI: 10.1016/j.media.2021.101978] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
12 Bartolucci M, Benelli M, Betti M, Bicchi S, Fedeli L, Giannelli F, Aquilini D, Baldini A, Consales G, Di Natale ME, Lotti P, Vannucchi L, Trezzi M, Mazzoni LN, Santini S, Carpi R, Matarrese D, Bernardi L, Mascalchi M; COVID Working Group. The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission. Sci Rep 2021;11:15619. [PMID: 34341411 DOI: 10.1038/s41598-021-95114-3] [Reference Citation Analysis]
13 Zhang F. Application of machine learning in CT images and X-rays of COVID-19 pneumonia. Medicine (Baltimore) 2021;100:e26855. [PMID: 34516488 DOI: 10.1097/MD.0000000000026855] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Romei C, Falaschi Z, Danna PSC, Airoldi C, Tonerini M, Rocchi E, Fanni SC, D'Amelio C, Barbieri G, Tiseo G, Arioli R, Paschè A, Karwoski RA, De Liperi A, Bartholmai BJ, Carriero A. Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis. Eur Radiol 2022. [PMID: 35028751 DOI: 10.1007/s00330-021-08485-6] [Reference Citation Analysis]
15 Nagaraj Y, de Jonge G, Andreychenko A, Presti G, Fink MA, Pavlov N, Quattrocchi CC, Morozov S, Veldhuis R, Oudkerk M, van Ooijen PMA. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting. Eur Radiol 2022. [PMID: 35362751 DOI: 10.1007/s00330-022-08730-6] [Reference Citation Analysis]
16 Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. J Am Med Inform Assoc 2021;28:2050-67. [PMID: 34151987 DOI: 10.1093/jamia/ocab098] [Reference Citation Analysis]
17 Laino ME, Ammirabile A, Lofino L, Lundon DJ, Chiti A, Francone M, Savevski V. Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence. Emerg Radiol 2022. [PMID: 35048222 DOI: 10.1007/s10140-021-02008-y] [Reference Citation Analysis]
18 Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, Kitamura FC, Lacey G, de Antônio Corradi GC, Nino G, Shin HH, Obinata H, Ren H, Crane JC, Tetreault J, Guan J, Garrett JW, Kaggie JD, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru MG, Haider MA, AbdelMaseeh M, Rieke N, Damasceno PF, E Silva PMC, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist TM, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon YJ, Quraini A, Feng A, Priest AN, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Díez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess CP, Compas C, Bhatia D, Oermann EK, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonça MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod SL, Reed S, Gräf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor VL, Rakvongthai Y, Lee YR, Wen Y, Gilbert FJ, Flores MG, Li Q. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med 2021;27:1735-43. [PMID: 34526699 DOI: 10.1038/s41591-021-01506-3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Deng H, Li X. AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review. Front Artif Intell 2021;4:612914. [PMID: 34368756 DOI: 10.3389/frai.2021.612914] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
20 Sarker S, Jamal L, Ahmed SF, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Rob Auton Syst 2021;146:103902. [PMID: 34629751 DOI: 10.1016/j.robot.2021.103902] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Shiri I, Sorouri M, Geramifar P, Nazari M, Abdollahi M, Salimi Y, Khosravi B, Askari D, Aghaghazvini L, Hajianfar G, Kasaeian A, Abdollahi H, Arabi H, Rahmim A, Radmard AR, Zaidi H. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput Biol Med 2021;132:104304. [PMID: 33691201 DOI: 10.1016/j.compbiomed.2021.104304] [Cited by in Crossref: 14] [Cited by in F6Publishing: 6] [Article Influence: 14.0] [Reference Citation Analysis]
22 Wang C, Huang L, Xiao S, Li Z, Ye C, Xia L, Zhou X. Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging. Eur J Nucl Med Mol Imaging 2021. [PMID: 34137946 DOI: 10.1007/s00259-021-05435-8] [Reference Citation Analysis]