A Novel Online Training Platform for Medical Image Interpretation
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-981-10-9035-6_153 http://hdl.handle.net/11449/184126 |
Resumo: | One of the major problems in the health area is false positive and negative diagnoses, especially in the interpretation of radiological images. Several papers affirm that the radiologist experience helps in accurate diagnosis, reducing inter-observer and intra-observer variability. We assume that the lack of training is causing this problem and if a good training process is on place can reduce the level of false positive and false negative diagnosis, and this training should start at the undergraduate level. Thus, this paper aims to show an online training platform applied to of interpretation imaging learning. The platform was developed using the php language and is hosted on the 000webhost server, consisting of an image base (format, png, jpg, tiff and DICOM), diagnostic imaging tests and user training quiz (students/residents) about radiographic images interpretation. The teacher can add images, prepare diagnostic tests and create questionnaires. The users perform the diagnostic tests and answer the questionnaires, obtaining a score in real time. This platform can be used inside and outside the classroom, where they can train the diagnosis by image to improve their knowledge. The platform was tested by 20 medical students that, after use it, answered the usability tests based on the SUS scale. The usability tests results showed that 90 of the users gave the maximum concept to the platform. |
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A Novel Online Training Platform for Medical Image InterpretationLearningInterpretation medical imagesImage diagnosticOne of the major problems in the health area is false positive and negative diagnoses, especially in the interpretation of radiological images. Several papers affirm that the radiologist experience helps in accurate diagnosis, reducing inter-observer and intra-observer variability. We assume that the lack of training is causing this problem and if a good training process is on place can reduce the level of false positive and false negative diagnosis, and this training should start at the undergraduate level. Thus, this paper aims to show an online training platform applied to of interpretation imaging learning. The platform was developed using the php language and is hosted on the 000webhost server, consisting of an image base (format, png, jpg, tiff and DICOM), diagnostic imaging tests and user training quiz (students/residents) about radiographic images interpretation. The teacher can add images, prepare diagnostic tests and create questionnaires. The users perform the diagnostic tests and answer the questionnaires, obtaining a score in real time. This platform can be used inside and outside the classroom, where they can train the diagnosis by image to improve their knowledge. The platform was tested by 20 medical students that, after use it, answered the usability tests based on the SUS scale. The usability tests results showed that 90 of the users gave the maximum concept to the platform.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Univ Mogi das Cruzes, Biomed Engn, Sao Paulo, BrazilSao Paulo State Univ, Inst Ciencia & Tecnol, Sorocaba, BrazilSao Paulo State Univ, Inst Ciencia & Tecnol, Sorocaba, BrazilFAPESP: 2017/14016-7SpringerUniv Mogi das CruzesUniversidade Estadual Paulista (Unesp)Silva, S. M. daRodrigues, S. C. M.Bissaco, M. A. S.Scardovelli, T.Boschi, S. R. M. S.Marques, M. A. [UNESP]Santos, M. F.Silva, A. P.Lhotska, L.Sukupova, L.Lackovic, IIbbott, G. S.2019-10-03T18:20:08Z2019-10-03T18:20:08Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject831-835http://dx.doi.org/10.1007/978-981-10-9035-6_153World Congress On Medical Physics And Biomedical Engineering 2018, Vol 1. New York: Springer, v. 68, n. 1, p. 831-835, 2019.1680-0737http://hdl.handle.net/11449/18412610.1007/978-981-10-9035-6_153WOS:000450908300153Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengWorld Congress On Medical Physics And Biomedical Engineering 2018, Vol 1info:eu-repo/semantics/openAccess2021-10-23T20:17:32Zoai:repositorio.unesp.br:11449/184126Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T20:17:32Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Novel Online Training Platform for Medical Image Interpretation |
title |
A Novel Online Training Platform for Medical Image Interpretation |
spellingShingle |
A Novel Online Training Platform for Medical Image Interpretation Silva, S. M. da Learning Interpretation medical images Image diagnostic |
title_short |
A Novel Online Training Platform for Medical Image Interpretation |
title_full |
A Novel Online Training Platform for Medical Image Interpretation |
title_fullStr |
A Novel Online Training Platform for Medical Image Interpretation |
title_full_unstemmed |
A Novel Online Training Platform for Medical Image Interpretation |
title_sort |
A Novel Online Training Platform for Medical Image Interpretation |
author |
Silva, S. M. da |
author_facet |
Silva, S. M. da Rodrigues, S. C. M. Bissaco, M. A. S. Scardovelli, T. Boschi, S. R. M. S. Marques, M. A. [UNESP] Santos, M. F. Silva, A. P. Lhotska, L. Sukupova, L. Lackovic, I Ibbott, G. S. |
author_role |
author |
author2 |
Rodrigues, S. C. M. Bissaco, M. A. S. Scardovelli, T. Boschi, S. R. M. S. Marques, M. A. [UNESP] Santos, M. F. Silva, A. P. Lhotska, L. Sukupova, L. Lackovic, I Ibbott, G. S. |
author2_role |
author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Univ Mogi das Cruzes Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Silva, S. M. da Rodrigues, S. C. M. Bissaco, M. A. S. Scardovelli, T. Boschi, S. R. M. S. Marques, M. A. [UNESP] Santos, M. F. Silva, A. P. Lhotska, L. Sukupova, L. Lackovic, I Ibbott, G. S. |
dc.subject.por.fl_str_mv |
Learning Interpretation medical images Image diagnostic |
topic |
Learning Interpretation medical images Image diagnostic |
description |
One of the major problems in the health area is false positive and negative diagnoses, especially in the interpretation of radiological images. Several papers affirm that the radiologist experience helps in accurate diagnosis, reducing inter-observer and intra-observer variability. We assume that the lack of training is causing this problem and if a good training process is on place can reduce the level of false positive and false negative diagnosis, and this training should start at the undergraduate level. Thus, this paper aims to show an online training platform applied to of interpretation imaging learning. The platform was developed using the php language and is hosted on the 000webhost server, consisting of an image base (format, png, jpg, tiff and DICOM), diagnostic imaging tests and user training quiz (students/residents) about radiographic images interpretation. The teacher can add images, prepare diagnostic tests and create questionnaires. The users perform the diagnostic tests and answer the questionnaires, obtaining a score in real time. This platform can be used inside and outside the classroom, where they can train the diagnosis by image to improve their knowledge. The platform was tested by 20 medical students that, after use it, answered the usability tests based on the SUS scale. The usability tests results showed that 90 of the users gave the maximum concept to the platform. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-03T18:20:08Z 2019-10-03T18:20:08Z 2019-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-981-10-9035-6_153 World Congress On Medical Physics And Biomedical Engineering 2018, Vol 1. New York: Springer, v. 68, n. 1, p. 831-835, 2019. 1680-0737 http://hdl.handle.net/11449/184126 10.1007/978-981-10-9035-6_153 WOS:000450908300153 |
url |
http://dx.doi.org/10.1007/978-981-10-9035-6_153 http://hdl.handle.net/11449/184126 |
identifier_str_mv |
World Congress On Medical Physics And Biomedical Engineering 2018, Vol 1. New York: Springer, v. 68, n. 1, p. 831-835, 2019. 1680-0737 10.1007/978-981-10-9035-6_153 WOS:000450908300153 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
World Congress On Medical Physics And Biomedical Engineering 2018, Vol 1 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
831-835 |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1799965339787722752 |