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Author (down) Albiol, A.; Albiol, F.; Paredes, R.; Plasencia-Martinez, J.M.; Blanco Barrio, A.; Garcia Santos, J.M.; Tortajada, S.; Gonzalez Montano, V.M.; Rodriguez Godoy, C.E.; Fernandez Gomez, S.; Oliver-Garcia, E.; de la Iglesia Vaya, M.; Marquez Perez, F.L.; Rayo Madrid, J.I. doi  openurl
  Title A comparison of Covid-19 early detection between convolutional neural networks and radiologists Type Journal Article
  Year 2022 Publication Insights into Imaging Abbreviated Journal Insights Imaging  
  Volume 13 Issue 1 Pages 122 - 12pp  
  Keywords Deep learning; Covid-19; Radiology  
  Abstract Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.  
  Address [Albiol, Alberto] Univ Politecn Valencia, iTeam Inst, ETSI Telecomunicac, Camino Vera S-N, Valencia 46022, Spain, Email: alalbiol@iteam.upv.es  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1869-4101 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000832727200003 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ pastor @ Serial 5302  
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Author (down) Agramunt, J. et al; Tain, J.L.; Albiol, F.; Algora, A.; Domingo-Pardo, C.; Jordan, M. D.; Rubio, B.; Tarifeño-Saldivia, A.; Valencia, E. doi  openurl
  Title Characterization of a neutron-beta counting system with beta-delayed neutron emitters Type Journal Article
  Year 2016 Publication Nuclear Instruments & Methods in Physics Research A Abbreviated Journal Nucl. Instrum. Methods Phys. Res. A  
  Volume 807 Issue Pages 69-78  
  Keywords Beta-delayed neutron emission probability; Neutron and beta counters; Self-triggered digital data acquisition system; Geant4 simulations  
  Abstract A new detection system for the measurement of beta-delayed neutron emission probabilities has been characterized using fission products with well known beta-delayed neutron emission properties. The setup consists of BELEN-20, a 4 pi-neutron counter with twenty He-3 proportional tubes arranged inside a large polyethylene neutron moderator, a thin Si detector for beta counting and a self-triggering digital data acquisition system. The use of delayed-neutron precursors with different neutron emission windows allowed the study of the effect of energy dependency on neutron, beta and beta-neutron rates. The observed effect is well reproduced by Monte Carlo simulations. The impact of this dependency on the accuracy of neutron emission probabilities is discussed. A new accurate value of the neutron emission probability for the important delayed-neutron precursor I-137 was obtained, P-n = 7.76(14)%.  
  Address [Agramunt, J.; Tain, J. L.; Albiol, E.; Algora, A.; Domingo-Pardo, C.; Jordan, M. D.; Rubio, B.; Tarifeno-Saldivia, A.; Valencia, E.] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46071 Valencia, Spain, Email: Tain@ific.uv.es  
  Corporate Author Thesis  
  Publisher Elsevier Science Bv Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0168-9002 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000365596200010 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 2481  
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ific federMinisterio de Ciencia e InnovaciĆ³nAgencia Estatal de Investigaciongva