Artificial Intelligence Performance on Digital Mammography, Synthetic Mammography, and Digital Breast Tomosynthesis: A Systematic Review.

Abstract

Objective This review aims to evaluate the effectiveness of AI in breast cancer screening by examining its performance across various modalities, including digital mammography (DM), digital breast tomosynthesis (DBT), and synthetic mammography (SM), as well as its ability to detect different breast densities and types of lesions. Method The Medline and Scopus databases were searched electronically from the earliest date to 9 August 2023 for studies that met the inclusion criteria. A QUADAS-2 tool was used to assess the quality of the included studies. The diagnostic performance metrics of included studies were reported, such as sensitivity, specificity, accuracy, and the area under the curve (AUC). A narrative synthesis with tables and figures was used to present the result. Results Based on the search, 13 studies were included. Most studies were published in 2021 (46%) and in the USA (4/13). A majority of the included studies used cross-sectional datasets to test their AI tools, and the USA was found to be the most common dataset location. Two studies developed AI tools to classify breast density, and the mean accuracy was 82% for DM and 80% for SM, whereas the AUC were 0.94 and 0.93, respectively. The remaining 11 studies focused on AI diagnostic applications. Four DM studies showed a sensitivity (mean: 73.7, median: 69.9, IQR: 14), two SM studies (mean: 79.9), and five DBT sensitivity studies (mean: 82.2, median: 84, IQR: 21).

Conclusion AI in breast classification has a comparable performance, while the DBT has the highest performance among modalities and techniques.