Artificial intelligence in mammography: a systematic review of the external validation

To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography. Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with...

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Bibliographic Details
Published in:	Revista Brasileira de ginecologia e obstetrícia Vol. 46
Main Authors:	Branco, Paulo Eduardo Souza Castelo, Franco, Adriane Helena Silva, de Oliveira, Amanda Prates, Carneiro, Isabela Maurício Costa, de Carvalho, Luciana Maurício Costa, de Souza, Jonathan Igor Nunes, Leandro, Danniel Rodrigo, Cândido, Eduardo Batista
Format:	Journal Article
Language:	English
Published:	Brazil Federação Brasileira das Sociedades de Ginecologia e Obstetrícia 01-01-2024
Subjects:	Algorithms Artificial Intelligence Breast neoplasms Breast Neoplasms - diagnostic imaging Deep learning Early Detection of Cancer - methods Female Humans Machine learning Mammography Mammography - methods OBSTETRICS & GYNECOLOGY Review Sensitivity and Specificity Validation Studies as Topic Deep learning Breast neoplasms Mammography Artificial Intelligence Machine learning
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Summary:	To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography. Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms "Artificial Intelligence," "Mammography," and their respective MeSH terms. We filtered publications from the past ten years (2014 - 2024) and in English. A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work. The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity. It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone. AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Undefined-1 ObjectType-Feature-3 ObjectType-Review-4 content type line 23 Conflicts to interest: none to declare. Associate Editor: Cassio Cardoso Filho (https://orcid.org/0000-0002-1895-0106) Universidade Estadual de Campinas, Campinas, SP, Brasil
ISSN:	0100-7203 1806-9339 1806-9339
DOI:	10.61622/rbgo/2024rbgo71