Early Results of Using AI in Mammography Screening for Breast Cancer

Abstract

<b>Background</b>: Recent advancements in Artificial Intelligence (AI) have the potential to address the challenges of mammographic screening programs by enhancing the performance of Computer-Aided Detection (CAD) systems, improving detection accuracy, and reducing false positive rates and recall rates. These systems were mostly investigated by control trials using cancer-enriched datasets and multiple readers. <b>Objectives</b>: This study aims to evaluate the real-world impact of AI integration on the performance of a breast cancer screening program. <b>Methods</b>: In January 2021, our mammography unit integrated an AI system (iCAD version 2.0) into its mammographic screening protocol. This study evaluates audit data of 31,176 mammograms interpreted between 2017 and 2021, comparing 24,373 mammograms prior to AI implementation and 6803 after the integration. Logistic regression analysis was used to assess the statistical significance of changes in key screening metrics, with a significance level of <i>p</i> < 0.05. <b>Results</b>: This study assesses the impact of artificial intelligence (AI) on mammographic screening. The cancer detection rate increased significantly from 6.2 per 1000 in 2019 to 9.3 per 1000 in 2021, with cancers detected on mammograms rising to 98%. Stage 1 cancer detection reached 100%, and the false negative rate dropped to 0%. Additionally, ductal carcinoma in situ (DCIS) detection decreased from 36.4% in 2019 to 20% in 2021. These findings highlight AI's effectiveness in improving cancer detection accuracy and efficiency. <b>Conclusions</b>: The integration of AI into mammographic screening demonstrated promising results in improving cancer detection rates and reducing false negative rates. These findings highlight AI's potential to enhance screening efficacy.

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