Abstract
Background
Artificial intelligence (AI)-based systems have the potential to increase the efficiency and effectiveness of breast cancer screening programs but need to be carefully validated before clinical implementation.
Purpose
To retrospectively evaluate an AI system to safely reduce the workload of a double-reading breast cancer screening program.
Material and Methods
All digital mammography (DM) screening examinations of women aged 40–74 years between August 2021 and January 2022 in Östergötland, Sweden were included. Analysis of the interval cancers (ICs) was performed in 2024. Each examination was double-read by two breast radiologists and processed by the AI system, which assigned a score of 1–10 to each examination based on increasing likelihood of cancer. In a retrospective simulation, the AI system was used for triaging; low-risk examinations (score 1–7) were selected for single reading and high-risk examinations (score 8–10) for double reading.
Results
A total of 15,468 DMs were included. Using an AI triaging strategy, 10,473 (67.7%) examinations received scores of 1–7, resulting in a 34% workload reduction. Overall, 52/53 screen-detected cancers were assigned a score of 8–10 by the AI system. One cancer was missed by the AI system (score 4) but was detected by the radiologists. In total, 11 cases of IC were found in the 2024 analysis.
Conclusion
Replacing one reader in breast cancer screening with an AI system for low-risk cases could safely reduce workload by 34%. In total, 11 cases of IC were found in the 2024 analysis; of them, three were identified correctly by the AI system at the 2021–2022 examination.
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