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Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images
(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have co...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316900/ https://www.ncbi.nlm.nih.gov/pubmed/35888048 http://dx.doi.org/10.3390/life12070958 |
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author | Florescu, Lucian Mihai Streba, Costin Teodor Şerbănescu, Mircea-Sebastian Mămuleanu, Mădălin Florescu, Dan Nicolae Teică, Rossy Vlăduţ Nica, Raluca Elena Gheonea, Ioana Andreea |
author_facet | Florescu, Lucian Mihai Streba, Costin Teodor Şerbănescu, Mircea-Sebastian Mămuleanu, Mădălin Florescu, Dan Nicolae Teică, Rossy Vlăduţ Nica, Raluca Elena Gheonea, Ioana Andreea |
author_sort | Florescu, Lucian Mihai |
collection | PubMed |
description | (1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals. |
format | Online Article Text |
id | pubmed-9316900 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93169002022-07-27 Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images Florescu, Lucian Mihai Streba, Costin Teodor Şerbănescu, Mircea-Sebastian Mămuleanu, Mădălin Florescu, Dan Nicolae Teică, Rossy Vlăduţ Nica, Raluca Elena Gheonea, Ioana Andreea Life (Basel) Article (1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals. MDPI 2022-06-26 /pmc/articles/PMC9316900/ /pubmed/35888048 http://dx.doi.org/10.3390/life12070958 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Florescu, Lucian Mihai Streba, Costin Teodor Şerbănescu, Mircea-Sebastian Mămuleanu, Mădălin Florescu, Dan Nicolae Teică, Rossy Vlăduţ Nica, Raluca Elena Gheonea, Ioana Andreea Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images |
title | Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images |
title_full | Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images |
title_fullStr | Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images |
title_full_unstemmed | Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images |
title_short | Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images |
title_sort | federated learning approach with pre-trained deep learning models for covid-19 detection from unsegmented ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316900/ https://www.ncbi.nlm.nih.gov/pubmed/35888048 http://dx.doi.org/10.3390/life12070958 |
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