Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

被引:53
|
作者
Daniel Lopez-Cabrera, Jose [1 ]
Orozco-Morales, Ruben [2 ]
Armando Portal-Diaz, Jorge [2 ]
Lovelle-Enriquez, Orlando [3 ]
Perez-Diaz, Marlen [2 ]
机构
[1] Univ Cent Marta Abreu Villas, Fac Matemat Fis & Comp, Ctr Invest Informat, Santa Clara, Villa Clara, Cuba
[2] Univ Cent Marta Abreu Las Villas, Fac Ingn Elect, Dept Control Automat, Santa Clara, Cuba
[3] Hosp Comandante Manuel Fajardo Rivero, Dept Imagenol, Santa Clara, Cuba
关键词
COVID-19; Chest X-rays; Artificial intelligence; Deep learning; CORONAVIRUS; INFECTION;
D O I
10.1007/s12553-021-00520-2
中图分类号
R-058 [];
学科分类号
摘要
The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.
引用
收藏
页码:411 / 424
页数:14
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