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Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus
Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensus
- Source :
- Radiol Artif Intell, Radiology. Artificial intelligence 4 (2022): e210199. doi:10.1148/ryai.210199, info:cnr-pdr/source/autori:Magni V.; Interlenghi M.; Cozzi A.; Alì M.; Salvatore C.; Azzena A.A.; Capra D.; Carriero S.; Della Pepa G.; Fazzini D.; Granata G.; Monti C.B.; Muscogiuri G.; Pellegrino G.; Schiaffino S.; Castiglioni I.; Papa S.; Sardanelli F./titolo:Development and Validation of an AI-driven Mammographic Breast Density Classification Tool Based on Radiologist Consensu/doi:10.1148%2Fryai.210199/rivista:Radiology. Artificial intelligence/anno:2022/pagina_da:e210199/pagina_a:/intervallo_pagine:e210199/volume:4
- Publication Year :
- 2022
- Publisher :
- Radiological Society of North America (RSNA), 2022.
-
Abstract
- Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords: Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022
Details
- ISSN :
- 26386100
- Volume :
- 4
- Database :
- OpenAIRE
- Journal :
- Radiology: Artificial Intelligence
- Accession number :
- edsair.doi.dedup.....614e23baa8a00952f1c258ede5b1c4d2
- Full Text :
- https://doi.org/10.1148/ryai.210199