Ultrasound (US) is a valuable imaging modality used to detect primary breast malignancy. However, radiologists have a limited ability to distinguish between benign and malignant lesions on US, leading to false-positive and false-negative results, which limit the positive predictive value of lesions sent for biopsy (PPV3) and specificity. A recent study demonstrated that incorporating an AI-based decision support (DS) system into US image analysis could help improve US diagnostic performance. While the DS system is promising, its efficacy in terms of its impact also needs to be measured when integrated into existing clinical workflows. The current study evaluates workflow schemas for DS integration and its impact on diagnostic accuracy. The impact on two different reading methodologies, sequential and independent, was assessed. This study demonstrates significant accuracy differences between the two workflow schemas as measured by area under the receiver operating curve (AUC), as well as inter-operator variability differences as measured by Kendall's tau-b. This evaluation has practical implications on the utilization of such technologies in diagnostic environments as compared to previous studies.
Keywords: Artificial intelligence; Breast cancer; Clinical workflow; Computer-aided diagnosis (CAD); Decision support; Machine learning.