Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://scielo.pt/scielo.php?script=sci_arttext&pid=S2341-45452021000200087 |
Resumo: | Abstract: Background: Video capsule endoscopy (VCE) revolutionized the diagnosis and management of obscure gastrointestinal bleeding, though the rate of detection of small bowel lesions by the physician is still disappointing. Our group developed a novel algorithm (CMEMS-Uminho) to automatically detect angioectasias which display greater accuracy in VCE static frames than other methods previously published. We aimed to evaluate the algorithm overall performance and assess its diagnostic yield and usability in clinical practice. Methods: Algorithm overall performance was determined using 54 full-length VCE recordings. To assess its diagnostic yield and usability in clinical practice, 38 VCE examinations with the clinical diagnosis of angioectasias consecutively performed (2017-2018) were evaluated by three physicians with diferente experiences. The CMEMS-Uminho algorithm was also applied. The performance of the CMEMS-Uminho algorithm was defined by a positive concordance between a frame automatically selected by the software and a study independente capsule endoscopist. Results: Overall performance in complete VCE recordings was 77.7%, and diagnostic yield was 94.7%. There were significant differences between physicians in regard to global detection rate (p < 0.001), detection rate per capsule (p < 0.001), diagnostic yield (p = 0.007), true positive rate (p < 0.001), time (p < 0.001), and speed viewing (p < 0.001). The application of CMEMS-Uminho algorithm significantly enhanced all readers’ global detection rate (p < 0.001) and the differences between them were no longer observed. Conclusion: The CMEMS-Uminho algorithm detained a good overall performance and was able to enhance physicians’ performance, suggesting a potential usability of this tool in clinical practice. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule EndoscopyVideo capsule endoscopyAngioectasiasAutomatic detectionAlgorithmAbstract: Background: Video capsule endoscopy (VCE) revolutionized the diagnosis and management of obscure gastrointestinal bleeding, though the rate of detection of small bowel lesions by the physician is still disappointing. Our group developed a novel algorithm (CMEMS-Uminho) to automatically detect angioectasias which display greater accuracy in VCE static frames than other methods previously published. We aimed to evaluate the algorithm overall performance and assess its diagnostic yield and usability in clinical practice. Methods: Algorithm overall performance was determined using 54 full-length VCE recordings. To assess its diagnostic yield and usability in clinical practice, 38 VCE examinations with the clinical diagnosis of angioectasias consecutively performed (2017-2018) were evaluated by three physicians with diferente experiences. The CMEMS-Uminho algorithm was also applied. The performance of the CMEMS-Uminho algorithm was defined by a positive concordance between a frame automatically selected by the software and a study independente capsule endoscopist. Results: Overall performance in complete VCE recordings was 77.7%, and diagnostic yield was 94.7%. There were significant differences between physicians in regard to global detection rate (p < 0.001), detection rate per capsule (p < 0.001), diagnostic yield (p = 0.007), true positive rate (p < 0.001), time (p < 0.001), and speed viewing (p < 0.001). The application of CMEMS-Uminho algorithm significantly enhanced all readers’ global detection rate (p < 0.001) and the differences between them were no longer observed. Conclusion: The CMEMS-Uminho algorithm detained a good overall performance and was able to enhance physicians’ performance, suggesting a potential usability of this tool in clinical practice.Sociedade Portuguesa de Gastrenterologia2021-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articletext/htmlhttp://scielo.pt/scielo.php?script=sci_arttext&pid=S2341-45452021000200087GE-Portuguese Journal of Gastroenterology v.28 n.2 2021reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttp://scielo.pt/scielo.php?script=sci_arttext&pid=S2341-45452021000200087Costa,DalilaVieira,PedroPinto,CatarinaArroja,BrunoLeal,TiagoMendes,SofiaGonçalves,RaquelLima,CarlosRolanda,Carlainfo:eu-repo/semantics/openAccess2024-02-06T17:34:09Zoai:scielo:S2341-45452021000200087Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:36:14.202282Repositório 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 |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy |
title |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy |
spellingShingle |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy Costa,Dalila Video capsule endoscopy Angioectasias Automatic detection Algorithm |
title_short |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy |
title_full |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy |
title_fullStr |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy |
title_full_unstemmed |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy |
title_sort |
Clinical Performance of New Software to Automatically Detect Angioectasias in Small Bowel Capsule Endoscopy |
author |
Costa,Dalila |
author_facet |
Costa,Dalila Vieira,Pedro Pinto,Catarina Arroja,Bruno Leal,Tiago Mendes,Sofia Gonçalves,Raquel Lima,Carlos Rolanda,Carla |
author_role |
author |
author2 |
Vieira,Pedro Pinto,Catarina Arroja,Bruno Leal,Tiago Mendes,Sofia Gonçalves,Raquel Lima,Carlos Rolanda,Carla |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Costa,Dalila Vieira,Pedro Pinto,Catarina Arroja,Bruno Leal,Tiago Mendes,Sofia Gonçalves,Raquel Lima,Carlos Rolanda,Carla |
dc.subject.por.fl_str_mv |
Video capsule endoscopy Angioectasias Automatic detection Algorithm |
topic |
Video capsule endoscopy Angioectasias Automatic detection Algorithm |
description |
Abstract: Background: Video capsule endoscopy (VCE) revolutionized the diagnosis and management of obscure gastrointestinal bleeding, though the rate of detection of small bowel lesions by the physician is still disappointing. Our group developed a novel algorithm (CMEMS-Uminho) to automatically detect angioectasias which display greater accuracy in VCE static frames than other methods previously published. We aimed to evaluate the algorithm overall performance and assess its diagnostic yield and usability in clinical practice. Methods: Algorithm overall performance was determined using 54 full-length VCE recordings. To assess its diagnostic yield and usability in clinical practice, 38 VCE examinations with the clinical diagnosis of angioectasias consecutively performed (2017-2018) were evaluated by three physicians with diferente experiences. The CMEMS-Uminho algorithm was also applied. The performance of the CMEMS-Uminho algorithm was defined by a positive concordance between a frame automatically selected by the software and a study independente capsule endoscopist. Results: Overall performance in complete VCE recordings was 77.7%, and diagnostic yield was 94.7%. There were significant differences between physicians in regard to global detection rate (p < 0.001), detection rate per capsule (p < 0.001), diagnostic yield (p = 0.007), true positive rate (p < 0.001), time (p < 0.001), and speed viewing (p < 0.001). The application of CMEMS-Uminho algorithm significantly enhanced all readers’ global detection rate (p < 0.001) and the differences between them were no longer observed. Conclusion: The CMEMS-Uminho algorithm detained a good overall performance and was able to enhance physicians’ performance, suggesting a potential usability of this tool in clinical practice. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-04-01 |
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://scielo.pt/scielo.php?script=sci_arttext&pid=S2341-45452021000200087 |
url |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S2341-45452021000200087 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S2341-45452021000200087 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Portuguesa de Gastrenterologia |
publisher.none.fl_str_mv |
Sociedade Portuguesa de Gastrenterologia |
dc.source.none.fl_str_mv |
GE-Portuguese Journal of Gastroenterology v.28 n.2 2021 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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1799137414263865344 |