Deep learning model-assisted detection of kidney stones on computed tomography
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | International Braz J Urol (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382022000500830 |
Resumo: | ABSTRACT Introduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0–1 cm, 1–2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined. Results: The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively. Conclusions: The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management. |
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Deep learning model-assisted detection of kidney stones on computed tomographyKidney CalculiTomography, X-Ray ComputedAlgorithmsArtificial IntelligenceABSTRACT Introduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0–1 cm, 1–2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined. Results: The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively. Conclusions: The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.Sociedade Brasileira de Urologia2022-10-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382022000500830International braz j urol v.48 n.5 2022reponame:International Braz J Urol (Online)instname:Sociedade Brasileira de Urologia (SBU)instacron:SBU10.1590/s1677-5538.ibju.2022.0132info:eu-repo/semantics/openAccessCaglayan,AlperHorsanali,Mustafa OzanKocadurdu,KenanIsmailoglu,ErenGuneyli,Serkaneng2022-08-19T00:00:00Zoai:scielo:S1677-55382022000500830Revistahttp://www.brazjurol.com.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||brazjurol@brazjurol.com.br1677-61191677-5538opendoar:2022-08-19T00:00International Braz J Urol (Online) - Sociedade Brasileira de Urologia (SBU)false |
dc.title.none.fl_str_mv |
Deep learning model-assisted detection of kidney stones on computed tomography |
title |
Deep learning model-assisted detection of kidney stones on computed tomography |
spellingShingle |
Deep learning model-assisted detection of kidney stones on computed tomography Caglayan,Alper Kidney Calculi Tomography, X-Ray Computed Algorithms Artificial Intelligence |
title_short |
Deep learning model-assisted detection of kidney stones on computed tomography |
title_full |
Deep learning model-assisted detection of kidney stones on computed tomography |
title_fullStr |
Deep learning model-assisted detection of kidney stones on computed tomography |
title_full_unstemmed |
Deep learning model-assisted detection of kidney stones on computed tomography |
title_sort |
Deep learning model-assisted detection of kidney stones on computed tomography |
author |
Caglayan,Alper |
author_facet |
Caglayan,Alper Horsanali,Mustafa Ozan Kocadurdu,Kenan Ismailoglu,Eren Guneyli,Serkan |
author_role |
author |
author2 |
Horsanali,Mustafa Ozan Kocadurdu,Kenan Ismailoglu,Eren Guneyli,Serkan |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Caglayan,Alper Horsanali,Mustafa Ozan Kocadurdu,Kenan Ismailoglu,Eren Guneyli,Serkan |
dc.subject.por.fl_str_mv |
Kidney Calculi Tomography, X-Ray Computed Algorithms Artificial Intelligence |
topic |
Kidney Calculi Tomography, X-Ray Computed Algorithms Artificial Intelligence |
description |
ABSTRACT Introduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0–1 cm, 1–2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined. Results: The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively. Conclusions: The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382022000500830 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382022000500830 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/s1677-5538.ibju.2022.0132 |
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 Brasileira de Urologia |
publisher.none.fl_str_mv |
Sociedade Brasileira de Urologia |
dc.source.none.fl_str_mv |
International braz j urol v.48 n.5 2022 reponame:International Braz J Urol (Online) instname:Sociedade Brasileira de Urologia (SBU) instacron:SBU |
instname_str |
Sociedade Brasileira de Urologia (SBU) |
instacron_str |
SBU |
institution |
SBU |
reponame_str |
International Braz J Urol (Online) |
collection |
International Braz J Urol (Online) |
repository.name.fl_str_mv |
International Braz J Urol (Online) - Sociedade Brasileira de Urologia (SBU) |
repository.mail.fl_str_mv |
||brazjurol@brazjurol.com.br |
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1750318078380474368 |