A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis

Detalhes bibliográficos
Autor(a) principal: Ibragimov, Bulat
Data de Publicação: 2023
Outros Autores: Arzamasov, Kirill, Maksudov, Bulat, Kiselev, Semen, Mongolin, Alexander, Mustafaev, Tamerlan, Ibragimova, Dilyara, Evteeva, Ksenia, Andreychenko, Anna, Morozov, Sergey
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/148762
Resumo: 18-71-10072. This grant went towards the framework development and deployment and manuscript preparation. The authors from PCCDTT received funding (No. in the Unified State Information System for Accounting of Research, Development, and Technological Works (EGISU): AAAA-A20-120071090056-3, АААА-А21-121012290079-2) under the Program of the Moscow Healthcare Department “Scientific Support of the Capital’s Healthcare” for 2020–2022.
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spelling A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosisGeneralSDG 3 - Good Health and Well-being18-71-10072. This grant went towards the framework development and deployment and manuscript preparation. The authors from PCCDTT received funding (No. in the Unified State Information System for Accounting of Research, Development, and Technological Works (EGISU): AAAA-A20-120071090056-3, АААА-А21-121012290079-2) under the Program of the Moscow Healthcare Department “Scientific Support of the Capital’s Healthcare” for 2020–2022.In 2020, an experiment testing AI solutions for lung X-ray analysis on a multi-hospital network was conducted. The multi-hospital network linked 178 Moscow state healthcare centers, where all chest X-rays from the network were redirected to a research facility, analyzed with AI, and returned to the centers. The experiment was formulated as a public competition with monetary awards for participating industrial and research teams. The task was to perform the binary detection of abnormalities from chest X-rays. For the objective real-life evaluation, no training X-rays were provided to the participants. This paper presents one of the top-performing AI frameworks from this experiment. First, the framework used two EfficientNets, histograms of gradients, Haar feature ensembles, and local binary patterns to recognize whether an input image represents an acceptable lung X-ray sample, meaning the X-ray is not grayscale inverted, is a frontal chest X-ray, and completely captures both lung fields. Second, the framework extracted the region with lung fields and then passed them to a multi-head DenseNet, where the heads recognized the patient’s gender, age and the potential presence of abnormalities, and generated the heatmap with the abnormality regions highlighted. During one month of the experiment from 11.23.2020 to 12.25.2020, 17,888 cases have been analyzed by the framework with 11,902 cases having radiological reports with the reference diagnoses that were unequivocally parsed by the experiment organizers. The performance measured in terms of the area under receiving operator curve (AUC) was 0.77. The AUC for individual diseases ranged from 0.55 for herniation to 0.90 for pneumothorax.NOVA Information Management School (NOVA IMS)RUNIbragimov, BulatArzamasov, KirillMaksudov, BulatKiselev, SemenMongolin, AlexanderMustafaev, TamerlanIbragimova, DilyaraEvteeva, KseniaAndreychenko, AnnaMorozov, Sergey2023-02-06T22:18:47Z2023-01-202023-01-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/148762eng2045-2322PURE: 52561666https://doi.org/10.1038/s41598-023-27397-7info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:30:25Zoai:run.unl.pt:10362/148762Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:28.856181Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
title A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
spellingShingle A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
Ibragimov, Bulat
General
SDG 3 - Good Health and Well-being
title_short A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
title_full A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
title_fullStr A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
title_full_unstemmed A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
title_sort A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis
author Ibragimov, Bulat
author_facet Ibragimov, Bulat
Arzamasov, Kirill
Maksudov, Bulat
Kiselev, Semen
Mongolin, Alexander
Mustafaev, Tamerlan
Ibragimova, Dilyara
Evteeva, Ksenia
Andreychenko, Anna
Morozov, Sergey
author_role author
author2 Arzamasov, Kirill
Maksudov, Bulat
Kiselev, Semen
Mongolin, Alexander
Mustafaev, Tamerlan
Ibragimova, Dilyara
Evteeva, Ksenia
Andreychenko, Anna
Morozov, Sergey
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Ibragimov, Bulat
Arzamasov, Kirill
Maksudov, Bulat
Kiselev, Semen
Mongolin, Alexander
Mustafaev, Tamerlan
Ibragimova, Dilyara
Evteeva, Ksenia
Andreychenko, Anna
Morozov, Sergey
dc.subject.por.fl_str_mv General
SDG 3 - Good Health and Well-being
topic General
SDG 3 - Good Health and Well-being
description 18-71-10072. This grant went towards the framework development and deployment and manuscript preparation. The authors from PCCDTT received funding (No. in the Unified State Information System for Accounting of Research, Development, and Technological Works (EGISU): AAAA-A20-120071090056-3, АААА-А21-121012290079-2) under the Program of the Moscow Healthcare Department “Scientific Support of the Capital’s Healthcare” for 2020–2022.
publishDate 2023
dc.date.none.fl_str_mv 2023-02-06T22:18:47Z
2023-01-20
2023-01-20T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/148762
url http://hdl.handle.net/10362/148762
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2045-2322
PURE: 52561666
https://doi.org/10.1038/s41598-023-27397-7
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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