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For: Li MD, Arun NT, Gidwani M, Chang K, Deng F, Little BP, Mendoza DP, Lang M, Lee SI, O'Shea A, Parakh A, Singh P, Kalpathy-Cramer J. Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks. Radiol Artif Intell 2020;2:e200079. [PMID: 33928256 DOI: 10.1148/ryai.2020200079] [Cited by in Crossref: 26] [Cited by in F6Publishing: 17] [Article Influence: 13.0] [Reference Citation Analysis]
Number Citing Articles
1 Degrave AJ, Janizek JD, Lee S. AI for radiographic COVID-19 detection selects shortcuts over signal. Nat Mach Intell 2021;3:610-9. [DOI: 10.1038/s42256-021-00338-7] [Cited by in Crossref: 24] [Cited by in F6Publishing: 5] [Article Influence: 24.0] [Reference Citation Analysis]
2 Park S, Kim G, Oh Y, Seo JB, Lee SM, Kim JH, Moon S, Lim JK, Ye JC. Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification. Med Image Anal 2022;75:102299. [PMID: 34814058 DOI: 10.1016/j.media.2021.102299] [Reference Citation Analysis]
3 Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2021. [PMID: 34336141 DOI: 10.1016/j.irbm.2021.07.002] [Reference Citation Analysis]
4 Esposito A, Casiraghi E, Chiaraviglio F, Scarabelli A, Stellato E, Plensich G, Lastella G, Di Meglio L, Fusco S, Avola E, Jachetti A, Giannitto C, Malchiodi D, Frasca M, Beheshti A, Robinson PN, Valentini G, Forzenigo L, Carrafiello G. Artificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical and a Qualitative Chest X-Ray Scoring System. RMI 2021;Volume 14:27-39. [DOI: 10.2147/rmi.s292314] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Yamada D, Ohde S, Imai R, Ikejima K, Matsusako M, Kurihara Y. Visual classification of three computed tomography lung patterns to predict prognosis of COVID-19: a retrospective study. BMC Pulm Med 2022;22:1. [PMID: 34980061 DOI: 10.1186/s12890-021-01813-y] [Reference Citation Analysis]
6 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Reference Citation Analysis]
7 Jiao Z, Choi JW, Halsey K, Tran TML, Hsieh B, Wang D, Eweje F, Wang R, Chang K, Wu J, Collins SA, Yi TY, Delworth AT, Liu T, Healey TT, Lu S, Wang J, Feng X, Atalay MK, Yang L, Feldman M, Zhang PJL, Liao WH, Fan Y, Bai HX. Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. Lancet Digit Health 2021;3:e286-94. [PMID: 33773969 DOI: 10.1016/S2589-7500(21)00039-X] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
8 Chen A, Zhao Z, Hou W, Singer AJ, Li H, Duong TQ. Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study. Front Med (Lausanne) 2021;8:661940. [PMID: 33996864 DOI: 10.3389/fmed.2021.661940] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Kulkarni AR, Athavale AM, Sahni A, Sukhal S, Saini A, Itteera M, Zhukovsky S, Vernik J, Abraham M, Joshi A, Amarah A, Ruiz J, Hart PD, Kulkarni H. Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19. BMJ Innov 2021;7:261-70. [PMID: 34192015 DOI: 10.1136/bmjinnov-2020-000593] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
10 Li MD, Arun NT, Aggarwal M, Gupta S, Singh P, Little BP, Mendoza DP, Corradi GCA, Takahashi MS, Ferraciolli SF, Succi MD, Lang M, Bizzo BC, Dayan I, Kitamura FC, Kalpathy-Cramer J. Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19. medRxiv 2020:2020. [PMID: 32995811 DOI: 10.1101/2020.09.15.20195453] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
11 Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021;11:1317. [PMID: 34441252 DOI: 10.3390/diagnostics11081317] [Reference Citation Analysis]
12 Afshar-Oromieh A, Prosch H, Schaefer-Prokop C, Bohn KP, Alberts I, Mingels C, Thurnher M, Cumming P, Shi K, Peters A, Geleff S, Lan X, Wang F, Huber A, Gräni C, Heverhagen JT, Rominger A, Fontanellaz M, Schöder H, Christe A, Mougiakakou S, Ebner L. A comprehensive review of imaging findings in COVID-19 - status in early 2021. Eur J Nucl Med Mol Imaging 2021;48:2500-24. [PMID: 33932183 DOI: 10.1007/s00259-021-05375-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
13 Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19. Diagnostics (Basel) 2021;11:1924. [PMID: 34679622 DOI: 10.3390/diagnostics11101924] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Rehouma R, Buchert M, Chen YP. Machine learning for medical imaging‐based COVID‐19 detection and diagnosis. Int J Intell Syst 2021;36:5085-115. [DOI: 10.1002/int.22504] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
15 Li MD, Little BP, Alkasab TK, Mendoza DP, Succi MD, Shepard JO, Lev MH, Kalpathy-Cramer J. Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs. Acad Radiol 2021;28:572-6. [PMID: 33485773 DOI: 10.1016/j.acra.2021.01.016] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Zheng R, Zheng Y, Dong-ye C, Nazir S. Improved 3D U-Net for COVID-19 Chest CT Image Segmentation. Scientific Programming 2021;2021:1-9. [DOI: 10.1155/2021/9999368] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
17 Karim AM, Kaya H, Alcan V, Sen B, Hadimlioglu IA. New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images. Symmetry 2022;14:1003. [DOI: 10.3390/sym14051003] [Reference Citation Analysis]
18 Kalaivani S, Seetharaman K. A THREE-STAGE ENSEMBLE BOOSTED CONVOLUTIONAL NEURAL NETWORK FOR CLASSIFICATION AND ANALYSIS OF COVID-19 CHEST X-RAY IMAGES. International Journal of Cognitive Computing in Engineering 2022. [DOI: 10.1016/j.ijcce.2022.01.004] [Reference Citation Analysis]
19 Shamout FE, Shen Y, Wu N, Kaku A, Park J, Makino T, Jastrzębski S, Witowski J, Wang D, Zhang B, Dogra S, Cao M, Razavian N, Kudlowitz D, Azour L, Moore W, Lui YW, Aphinyanaphongs Y, Fernandez-Granda C, Geras KJ. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. NPJ Digit Med 2021;4:80. [PMID: 33980980 DOI: 10.1038/s41746-021-00453-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
20 Gasqui MA, Pérard M, Decup F, Monsarrat P, Turpin YL, Villat C, Gueyffier F, Maucort-Boulch D, Roche L, Grosgogeat B. Place of a new radiological index in predicting pulp exposure before intervention for deep carious lesions. Oral Radiol 2021. [PMID: 33954908 DOI: 10.1007/s11282-021-00530-w] [Reference Citation Analysis]
21 Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-rivero AI, Etmann C, Mccague C, Beer L, Weir-mccall JR, Teng Z, Gkrania-klotsas E, Rudd JHF, Sala E, Schönlieb C; AIX-COVNET. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 2021;3:199-217. [DOI: 10.1038/s42256-021-00307-0] [Cited by in Crossref: 65] [Cited by in F6Publishing: 13] [Article Influence: 65.0] [Reference Citation Analysis]
22 Born J, Beymer D, Rajan D, Coy A, Mukherjee VV, Manica M, Prasanna P, Ballah D, Guindy M, Shaham D, Shah PL, Karteris E, Robertus JL, Gabrani M, Rosen-Zvi M. On the role of artificial intelligence in medical imaging of COVID-19. Patterns (N Y) 2021;2:100269. [PMID: 33969323 DOI: 10.1016/j.patter.2021.100269] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 Lang M, Li MD, Jiang KZ, Yoon BC, Mendoza DP, Flores EJ, Rincon SP, Mehan WA Jr, Conklin J, Huang SY, Lang AL, Giao DM, Leslie-Mazwi TM, Kalpathy-Cramer J, Little BP, Buch K. Severity of Chest Imaging is Correlated with Risk of Acute Neuroimaging Findings among Patients with COVID-19. AJNR Am J Neuroradiol 2021;42:831-7. [PMID: 33541897 DOI: 10.3174/ajnr.A7032] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Little BP. Disease Severity Scoring for COVID-19: A Welcome (Semi)Quantitative Role for Chest Radiography. Radiology 2021;:212212. [PMID: 34519581 DOI: 10.1148/radiol.2021212212] [Reference Citation Analysis]
25 Gibson LE, Fenza RD, Lang M, Capriles MI, Li MD, Kalpathy-Cramer J, Little BP, Arora P, Mueller AL, Ichinose F, Bittner EA, Berra L, G Chang M. Right Ventricular Strain Is Common in Intubated COVID-19 Patients and Does Not Reflect Severity of Respiratory Illness. J Intensive Care Med 2021;36:900-9. [PMID: 33783269 DOI: 10.1177/08850666211006335] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
26 Casiraghi E, Malchiodi D, Trucco G, Frasca M, Cappelletti L, Fontana T, Esposito AA, Avola E, Jachetti A, Reese J, Rizzi A, Robinson PN, Valentini G. Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments. IEEE Access 2020;8:196299-325. [PMID: 34812365 DOI: 10.1109/ACCESS.2020.3034032] [Cited by in Crossref: 16] [Article Influence: 16.0] [Reference Citation Analysis]
27 Arun N, Gaw N, Singh P, Chang K, Aggarwal M, Chen B, Hoebel K, Gupta S, Patel J, Gidwani M, Adebayo J, Li MD, Kalpathy-Cramer J. Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. Radiol Artif Intell 2021;3:e200267. [PMID: 34870212 DOI: 10.1148/ryai.2021200267] [Cited by in Crossref: 4] [Article Influence: 4.0] [Reference Citation Analysis]
28 De Moura J, Garcia LR, Vidal PFL, Cruz M, Lopez LA, Lopez EC, Novo J, Ortega M. Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices. IEEE Access 2020;8:195594-607. [PMID: 34786295 DOI: 10.1109/ACCESS.2020.3033762] [Cited by in Crossref: 13] [Article Influence: 13.0] [Reference Citation Analysis]
29 Karbhari Y, Basu A, Geem ZW, Han GT, Sarkar R. Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach. Diagnostics (Basel) 2021;11:895. [PMID: 34069841 DOI: 10.3390/diagnostics11050895] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
30 Signoroni A, Savardi M, Benini S, Adami N, Leonardi R, Gibellini P, Vaccher F, Ravanelli M, Borghesi A, Maroldi R, Farina D. BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset. Med Image Anal 2021;71:102046. [PMID: 33862337 DOI: 10.1016/j.media.2021.102046] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]