Research Projects
COVID-19 diagnostic and prognostic through Deep Learning on X-ray and CT images
Motivation
The worsening of COVID-19 disease occurs quickly in a portion of the contaminated, and can happen before 48 hours. Recent scientific publications show the use of medical images such as computed tomography and X-rays to aid in diagnosis, prognosis and treatment management. With the growth of cases, the work of the health team becomes more and more strenuous. The use of a tool that analyzes the images and highlights the clinical findings in the images streamlines the specific analysis, saving time and physical tiredness. In addition, this type of tool can be vital as an aid in decision-making in health facilities where there is no specialist, and remote care is the only alternative. An accurate prognosis and the decision on the best course of action to be taken in each case can be decisive in a scenario of uncertainty and, especially, of scarce resources.
Purpose
To develop computational methods and tools for prognosis of patients with COVID-19 using computed tomography and X-ray images
Bibliography
Motivation
The worsening of COVID-19 disease occurs quickly in a portion of the contaminated, and can happen before 48 hours. Recent scientific publications show the use of medical images such as computed tomography and X-rays to aid in diagnosis, prognosis and treatment management. With the growth of cases, the work of the health team becomes more and more strenuous. The use of a tool that analyzes the images and highlights the clinical findings in the images streamlines the specific analysis, saving time and physical tiredness. In addition, this type of tool can be vital as an aid in decision-making in health facilities where there is no specialist, and remote care is the only alternative. An accurate prognosis and the decision on the best course of action to be taken in each case can be decisive in a scenario of uncertainty and, especially, of scarce resources.
Purpose
To develop computational methods and tools for prognosis of patients with COVID-19 using computed tomography and X-ray images
Bibliography
- LI, L. et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology, Radiological Society of North America, p.200905, 2020.
- WANG, L.; WONG, A. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images. arXiv preprint 2020. Disponível em: arXiv:2003.09871
