Including temporal changes information to an AI system for breast cancer detection to reduce false positive rate

In breast cancer detection, change in findings throughout time is one of the major biomarkers for the presence of malignancy. Several studies have established the value of comparing mammograms with the ones from previous examinations. Some of them have shown that such comparison decreases the recall rate and increases the biopsy yield of cancer but does not increase the cancer detection rate. This evidence brought us to do the hypotheses that, as for human radiologists, adding temporal context information could be beneficial also for artificial intelligence (AI) systems for breast cancer detection thus improving their specificity which today represents the major limitation for an autonomous use of such AI systems. In this study we carry out a comparison between an AI system for breast cancer detection and an update version of the same system able to integrate the temporal context information.