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For: Tan HB, Xiong F, Jiang YL, Huang WC, Wang Y, Li HH, You T, Fu TT, Lu R, Peng BW. The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia. Sci Rep 2020;10:18926. [PMID: 33144676 DOI: 10.1038/s41598-020-76141-y] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
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2 Yunus MM, Mohamed Yusof AK, Ab Rahman MZ, Koh XJ, Sabarudin A, Nohuddin PNE, Ng KH, Kechik MMA, Karim MKA. Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics 2022;12:1660. [DOI: 10.3390/diagnostics12071660] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Mehrpouyan M, Zamanian H, Mehri-Kakavand G, Pursamimi M, Shalbaf A, Ghorbani M, Abbaskhani Davanloo A. Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach. Phys Eng Sci Med 2022. [PMID: 35796865 DOI: 10.1007/s13246-022-01140-4] [Reference Citation Analysis]
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5 Gillman AG, Lunardo F, Prinable J, Belous G, Nicolson A, Min H, Terhorst A, Dowling JA. Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review. Phys Eng Sci Med 2021. [PMID: 34919204 DOI: 10.1007/s13246-021-01093-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
6 Shiri I, Salimi Y, Saberi A, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Akhavanallaf A, Mostafaei S, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khosravi B, Khateri M, Bijari S, Atashzar MR, Shayesteh SP, Babaei MR, Jenabi E, Hasanian M, Shahhamzeh A, Gholami SYF, Mozafari A, Shirzad-aski H, Movaseghi F, Bozorgmehr R, Goharpey N, Abdollahi H, Geramifar P, Radmard AR, Arabi H, Rezaei-kalantari K, Oveisi M, Rahmim A, Zaidi H. Diagnosis of COVID-19 Using CT image Radiomics Features: A Comprehensive Machine Learning Study Involving 26,307 Patients.. [DOI: 10.1101/2021.12.07.21267367] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
7 Stammes MA, Lee JH, Meijer L, Naninck T, Doyle-Meyers LA, White AG, Borish HJ, Hartman AL, Alvarez X, Ganatra S, Kaushal D, Bohm RP, le Grand R, Scanga CA, Langermans JAM, Bontrop RE, Finch CL, Flynn JL, Calcagno C, Crozier I, Kuhn JH. Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates. Trends Mol Med 2021:S1471-4914(21)00316-6. [PMID: 34955425 DOI: 10.1016/j.molmed.2021.12.001] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
8 Tian C, Wang Y, Ma X, Chen Z, Xue H. Chiller Fault Diagnosis Based on Automatic Machine Learning. Front Energy Res 2021;9. [DOI: 10.3389/fenrg.2021.753732] [Reference Citation Analysis]
9 Pal A, Ali A, Young TR, Oostenbrink J, Prabhakar A, Prabhakar A, Deacon N, Arnold A, Eltayeb A, Yap C, Young DM, Tang A, Lakshmanan S, Lim YY, Pokarowski M, Kakodkar P. Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic. World J Radiol 2021; 13(9): 258-282 [PMID: 34630913 DOI: 10.4329/wjr.v13.i9.258] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [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] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
11 Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, Valdesi C, Croce P, Mastrodicasa D, Villani M, Trebeschi S, Serafini FL, Rosa C, Cocco G, Luberti R, Conte S, Mazzamurro L, Mereu M, Patea RL, Panara V, Marinari S, Vecchiet J, Caulo M. Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease. Sci Rep 2021;11:17237. [PMID: 34446812 DOI: 10.1038/s41598-021-96755-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
12 Kao YS, Lin KT. A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia. Diagnostics (Basel) 2021;11:991. [PMID: 34072573 DOI: 10.3390/diagnostics11060991] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
13 Xie CY, Pang CL, Chan B, Wong EY, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 2021;13:2469. [PMID: 34069367 DOI: 10.3390/cancers13102469] [Cited by in Crossref: 4] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
14 Moezzi M, Shirbandi K, Shahvandi HK, Arjmand B, Rahim F. The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. Inform Med Unlocked 2021;24:100591. [PMID: 33977119 DOI: 10.1016/j.imu.2021.100591] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
15 Xiong F, Wang Y, You T, Li HH, Fu TT, Tan H, Huang W, Jiang Y. The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT. Medicine (Baltimore) 2021;100:e25307. [PMID: 33761733 DOI: 10.1097/MD.0000000000025307] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
16 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: 59] [Cited by in F6Publishing: 30] [Article Influence: 59.0] [Reference Citation Analysis]