Abstract Breast cancer is the leading cause of cancer-related deaths among women in Brazil, highlighting the importance of early detection to improve outcomes. Artificial intelligence (AI) has garnered considerable attention for its potential to enhance breast cancer screening by reducing unnecessary exams, minimizing diagnostic errors, and increasing efficiency and accuracy—exemplified by advanced tools like Mirai. The present retrospective study analyzed 1,000 patients who underwent bilateral mammography from December 2019 to April 2024 at Hospital Santa Casa de Porto Alegre. All mammograms extracted in digital imaging and communications in medicine (DICOM) format were anonymized and processed by the Mirai algorithm to generate risk scores. Predictive performance was evaluated using discrimination metrics, such as C-index and area under the curve (AUC), as well as threshold analyses (F1-score, Youden's J) to estimate cancer risk. Mirai obtained a C-index of 0.76 (95% CI: 0.72–0.80) and an AUC of 0.81. The analysis further evaluated the F1-score and Youden's J statistic in an attempt to establish risk thresholds for cancer development. The results suggest that the Mirai model holds promise as a valuable tool for breast cancer detection, particularly for early identification of high-risk patients.
Zereu et al. (Sun,) studied this question.