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dc.contributor.author
Tajerian, Matías N.
dc.contributor.author
Pesce, Karina
dc.contributor.author
Frangella, Julia
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Quiroga, Ezequiel
dc.contributor.author
Boietti, Bruno Rafael
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dc.contributor.author
Chico, Maria José
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Swiecicki, María Paz
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Benitez, Sonia
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Rabellino, Martín
dc.contributor.author
Luna, Daniel Roberto
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dc.date.available
2022-07-05T13:58:51Z
dc.date.issued
2021-06
dc.identifier.citation
Tajerian, Matías N.; Pesce, Karina; Frangella, Julia; Quiroga, Ezequiel; Boietti, Bruno Rafael; et al.; Artemisia: Validation of a deep learning model for automatic breast density categorization; AME Publishing Company; Journal of Medical Artificial Intelligence; 4; June; 6-2021; 1-9
dc.identifier.issn
2617-2496
dc.identifier.uri
http://hdl.handle.net/11336/161290
dc.description.abstract
Background: The aim of this study is to validate a deep learning model for the classification of breast density according to American College of Radiology’s breast density patterns. Methods: A convolutional neural network was developed with 10,229 digital screening mammogram images. Once the network was developed and tested, its performance was evaluated before a group of six professionals, the majority report and a commercial software application. We selected randomly 451 new mammographic images from different studies and patients. The categorization process by professionals was repeated in two stages. Results: The agreement between the convolutional neural network and the majority report was k=0.64 (95% CI: 0.58–0.69) in the first stage and k=0.57 (95% CI: 0.52–0.63) in the second stage. The agreement between the CNN and the commercial software application was k=0.54 (95% CI: 0.48–0.60). In both cases, we observed that the concordances of the CNN were within or above the range of professionals’ concordances values. Conclusions: Considering the internal reference standard (majority report) and the external reference standard (commercial software application), we can affirm the CNN achieved professional level performance.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
AME Publishing Company
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
ALGORITHM DEVELOPMENT
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ARTIFICIAL INTELLIGENCE
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BREAST DENSITY
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DEEP LEARNING
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MEDICAL IMAGING
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Otras Ciencias de la Salud
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dc.subject.classification
Ciencias de la Salud
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Artemisia: Validation of a deep learning model for automatic breast density categorization
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-07-04T19:43:47Z
dc.journal.volume
4
dc.journal.number
June
dc.journal.pagination
1-9
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Tajerian, Matías N.. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
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Fil: Pesce, Karina. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
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Fil: Frangella, Julia. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
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Fil: Quiroga, Ezequiel. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
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Fil: Boietti, Bruno Rafael. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
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Fil: Chico, Maria José. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
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Fil: Swiecicki, María Paz. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
dc.description.fil
Fil: Benitez, Sonia. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
dc.description.fil
Fil: Rabellino, Martín. Hospital Italiano. Instituto Universitario. Escuela de Medicina; Argentina
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Fil: Luna, Daniel Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional e Ingeniería Biomédica - Hospital Italiano. Instituto de Medicina Traslacional e Ingeniería Biomédica.- Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional e Ingeniería Biomédica; Argentina
dc.journal.title
Journal of Medical Artificial Intelligence
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.21037/jmai-20-43
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://jmai.amegroups.com/article/view/6302/html
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