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Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C
Background: The elimination of the Hepatitis C virus (HCV) will only be possible if rapid and efficient actions are taken. Artificial neural networks (ANNs) are computing systems based on the topology of the biological brain, containing connected artificial neurons that can be tasked with solving me...
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/PMC8871056/ https://www.ncbi.nlm.nih.gov/pubmed/35204437 http://dx.doi.org/10.3390/diagnostics12020346 |
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author | Butaru, Anca Elena Mămuleanu, Mădălin Streba, Costin Teodor Doica, Irina Paula Diculescu, Mihai Mircea Gheonea, Dan Ionuț Oancea, Carmen Nicoleta |
author_facet | Butaru, Anca Elena Mămuleanu, Mădălin Streba, Costin Teodor Doica, Irina Paula Diculescu, Mihai Mircea Gheonea, Dan Ionuț Oancea, Carmen Nicoleta |
author_sort | Butaru, Anca Elena |
collection | PubMed |
description | Background: The elimination of the Hepatitis C virus (HCV) will only be possible if rapid and efficient actions are taken. Artificial neural networks (ANNs) are computing systems based on the topology of the biological brain, containing connected artificial neurons that can be tasked with solving medical problems. Aim: We expanded the previously presented HCV micro-elimination project started in September 2020 that aimed to identify HCV infection through coordinated screening in asymptomatic populations and developed two ANN models able to identify at-risk subjects selected through a targeted questionnaire. Material and method: Our study included 14,042 screened participants from a southwestern region of Oltenia, Romania. Each participant completed a 12-item questionnaire along with anti-HCV antibody rapid testing. Hepatitis-C-positive subjects were linked to care and ultimately could receive antiviral treatment if they had detectable viremia. We built two ANNs, trained and tested on the dataset derived from the questionnaires and then used to identify patients in a similar, already existing dataset. Results: We found 114 HCV-positive patients (81 females), resulting in an overall prevalence of 0.81%. We identified sharing personal hygiene items, receiving blood transfusions, having dental work or surgery and re-using hypodermic needles as significant risk factors. When used on an existing dataset of 15,140 persons (119 HCV cases), the first ANN models correctly identified 97 (81.51%) HCV-positive subjects through 13,401 tests, while the second ANN model identified 81 (68.06%) patients through only 5192 tests. Conclusions: The use of ANNs in selecting screening candidates may improve resource allocation and prioritize cases more prone to severe disease. |
format | Online Article Text |
id | pubmed-8871056 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88710562022-02-25 Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C Butaru, Anca Elena Mămuleanu, Mădălin Streba, Costin Teodor Doica, Irina Paula Diculescu, Mihai Mircea Gheonea, Dan Ionuț Oancea, Carmen Nicoleta Diagnostics (Basel) Article Background: The elimination of the Hepatitis C virus (HCV) will only be possible if rapid and efficient actions are taken. Artificial neural networks (ANNs) are computing systems based on the topology of the biological brain, containing connected artificial neurons that can be tasked with solving medical problems. Aim: We expanded the previously presented HCV micro-elimination project started in September 2020 that aimed to identify HCV infection through coordinated screening in asymptomatic populations and developed two ANN models able to identify at-risk subjects selected through a targeted questionnaire. Material and method: Our study included 14,042 screened participants from a southwestern region of Oltenia, Romania. Each participant completed a 12-item questionnaire along with anti-HCV antibody rapid testing. Hepatitis-C-positive subjects were linked to care and ultimately could receive antiviral treatment if they had detectable viremia. We built two ANNs, trained and tested on the dataset derived from the questionnaires and then used to identify patients in a similar, already existing dataset. Results: We found 114 HCV-positive patients (81 females), resulting in an overall prevalence of 0.81%. We identified sharing personal hygiene items, receiving blood transfusions, having dental work or surgery and re-using hypodermic needles as significant risk factors. When used on an existing dataset of 15,140 persons (119 HCV cases), the first ANN models correctly identified 97 (81.51%) HCV-positive subjects through 13,401 tests, while the second ANN model identified 81 (68.06%) patients through only 5192 tests. Conclusions: The use of ANNs in selecting screening candidates may improve resource allocation and prioritize cases more prone to severe disease. MDPI 2022-01-29 /pmc/articles/PMC8871056/ /pubmed/35204437 http://dx.doi.org/10.3390/diagnostics12020346 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 Butaru, Anca Elena Mămuleanu, Mădălin Streba, Costin Teodor Doica, Irina Paula Diculescu, Mihai Mircea Gheonea, Dan Ionuț Oancea, Carmen Nicoleta Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C |
title | Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C |
title_full | Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C |
title_fullStr | Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C |
title_full_unstemmed | Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C |
title_short | Resource Management through Artificial Intelligence in Screening Programs—Key for the Successful Elimination of Hepatitis C |
title_sort | resource management through artificial intelligence in screening programs—key for the successful elimination of hepatitis c |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871056/ https://www.ncbi.nlm.nih.gov/pubmed/35204437 http://dx.doi.org/10.3390/diagnostics12020346 |
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