Artificial intelligence for triaging of breast cancer screening mammograms and workload reduction: A meta-analysis of a deep learning software

Objective: Deep learning (DL) has shown promising results for improving mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) on the breast cancer screening process has not yet been fully elucidated in terms of potential workload reduction. We aim to assess if AI-based triaging of breast cancer screening mammograms could reduce the radiologist's workload with non-inferior sensitivity. Methods: PubMed, EMBASE, Cochrane Central, and Web of Science databases were systematically searched for studies that evaluated AI algorithms on computer-aided triage of breast cancer screening mammograms. We extracted data from homogenous studies and performed a proportion meta-analysis with a random-effects model to examine the radiologist's workload reduction (proportion of low-risk mammograms that could be theoretically ruled out from human's assessment) and the software's sensitivity to breast cancer detection. Results: Thirteen studies were selected for full review, and three studies that used the same commercially available DL algorithm were included in the meta-analysis. In the 156,852 examinations included, the threshold of 7 was identified as optimal. With these parameters, radiologist workload decreased by 68.3% (95%CI 0.655–0.711, I² = 98.76%, p < 0.001), while achieving a sensitivity of 93.1% (95%CI 0.882–0.979, I² = 83.86%, p = 0.002) and a specificity of 68.7% (95% CI 0.684–0.723, I² = 97.5%, p < 0.01). Conclusions: The deployment of DL computer-aided triage of breast cancer screening mammograms reduces the radiology workload while maintaining high sensitivity. Although the implementation of AI remains complex and heterogeneous, it is a promising tool to optimize healthcare resources.