Artificial Intelligence As a Second Reader for Screening Mammography

Abstract Background Artificial intelligence (AI) has shown promise in mammography interpretation, and its use as a second reader in breast cancer screening may reduce burden on healthcare systems. Purpose To evaluate the performance differences between routine double read and an AI as a second reader workflow (AISR) where the second reader is replaced with AI. Materials and Methods A cohort of patients undergoing routine breast cancer screening at a single center with mammography was retrospectively collected between 2005 and 2021. A model developed on US and UK data was fine tuned on Japanese data. We subsequently performed a reader study with ten qualified readers with varied experience (five reader pairs), comparing routine double read to an AISR workflow. Results A ‘test set’ of 4059 women (mean age 56 ± 14 years; 157 positive, 3902 negative) was collected, with 278 (mean age 55 ± 13 years; 90 positive, 188 negative) evaluated for the reader study. We demonstrate an AUC=.84 (95%CI: 0.805-0.881), on the test set, with no significant difference to decisions made in clinical practice (p=.32). Compared with routine double reading, in the AISR arm sensitivity improved by 7.6% (95%CI: 3.80-11.4, p=.00004) and specificity decreased 3.4% (1.42-5.43, p=.0016), with 71% (212/298) of scans no longer requiring input from a second reader. Variation in recall decision between reader pairs improved from a Cohen’s kappa of κ=.65 (96% CI, .61-.68) to κ=.74 (96% CI, .71-.77) in the AISR arm. Conclusion AISR improves sensitivity, reduces variability and decreases workload compared to routine dual screening.