Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging.
José Daniel López-CabreraRuben Orozco-MoralesJorge Armando Portal-DíazOrlando Adolfo LovelleMarlén Pérez-DíazPublished in: Health and technology (2021)
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.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- convolutional neural network
- big data
- coronavirus disease
- high resolution
- sars cov
- healthcare
- mental health
- systematic review
- randomized controlled trial
- dual energy
- emergency department
- computed tomography
- patient safety
- respiratory syndrome coronavirus
- magnetic resonance
- magnetic resonance imaging
- adverse drug
- neural network