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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals

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Author(s):
Carmo, Diedre [1] ; Campiotti, Israel [1, 2] ; Rodrigues, Livia [1] ; Fantini, Irene [1] ; Pinheiro, Gustavo [1] ; Moraes, Daniel [2] ; Nogueira, Rodrigo [1, 2] ; Rittner, Leticia [1] ; Lotufo, Roberto [1, 2]
Total Authors: 9
Affiliation:
[1] Univ Estadual Campinas, MICLab, Sch Elect & Comp Engn, Av Albert Einstein 400 Cidade Univ, BR-13083852 Campinas, SP - Brazil
[2] NeuralMind Inteligencia Artificial, Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: HEALTH INFORMATICS JOURNAL; v. 27, n. 3 JUL 2021.
Web of Science Citations: 0
Abstract

The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from Sao Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT. (AU)

FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
Grantee:Fernando Cendes
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 19/21964-4 - Deep learning for hierarchical multimodal neuroanatomy segmentation in brain tumor patients
Grantee:Diedre Santos do Carmo
Support type: Scholarships in Brazil - Doctorate