Computational methods applied to quality control of mammography images generated from ACR phantoms: An integrative review of current methodologies




Artificial Intelligence, Mammography, Quality Control, Image processing, ACR Phantom


This study aims to conduct an integrative review of research on computational methods used for mammography quality control while addressing the issue of subjectivity in existing quality control processes. We conducted an integrative search in three electronic databases to achieve our objective. Our search included studies published within the last eleven years, with a specific focus on original research that highlights the application of computational methods for assessing mammography image quality. The selected studies have been meticulously categorized based on the methodologies employed, the input variables used for image quality assessment and the overall quality of the findings. This categorization offers a holistic overview of the current state of research in this field. Our comprehensive review of these studies underscores the immense potential of automated systems designed to enhance image quality assurance in mammography. These computational methods offer a promising solution to mitigate subjectivity issues in the quality control process related to the reading of the image. By doing so, they hold the promise of improving clinical routines and ensuring the reliability of mammography diagnostics.


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Como Citar

Cecchetto, B., Carla Diniz Lopes Becker, Thatiane Alves Pianoschi, Alexandre Bacelar, Rochelle Lykawka, Janine Hastenteufel Dias, & Viviane Rodrigues Botelho. (2024). Computational methods applied to quality control of mammography images generated from ACR phantoms: An integrative review of current methodologies. Revista Brasileira De Física Médica, 18, 761.



Artigo de Revisão