Abstract P3-04-06: Artificial intelligence-based triaging of breast cancer screening mammograms and radiologist workload reduction: a systematic review and meta-analysis

Abstract

Abstract Introduction: Deep learning (DL) has shown promising results for mammographic breast cancer diagnosis. However, the impact of artificial intelligence (AI) in the screening process have not yet been fully addressed in terms of workload reduction, which has potential to decrease healthcare disparities. Radiologists are tasked with overwhelming volumes of screening mammograms, particularly in medically underserved areas. Therefore, when applied to workflow improvement, AI might be a tool to reduce healthcare disparities. The purpose of this systematic review and meta-analysis was 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 random-effects model to verify the radiologist’s workload reduction and the software’s sensitivity. Results: A total of 14 studies were systematically selected. Three studies using the same commercially available DL algorithm were included in the meta-analysis, with 156852 examinations evaluated at the threshold of 8. The radiologist’s workload decreased by 68.3% (95%CI 0.655-0.711, I² = 98.76%, p < 0.001), with a sensitivity of 93.1% (95%CI 0.882-0.979, I² = 83.86%, p = 0.002). Conclusion: Our findings suggest that DL computer-aided triage of breast cancer screening mammograms significantly reduces the radiologist’s workload with high sensitivity. Although AI’s implementation remains complex and heterogeneous, it is a promising tool to optimize healthcare resources with a potential large impact in low resource settings that struggle with workforce shortage. Citation Format: Debora Xavier, Isabele A. Miyawaki, Carlos Alberto Campello Jorge, Matheus Jose Barbosa Moreira, Bruno M. Carvalho, Felipe Batalini. Artificial intelligence-based triaging of breast cancer screening mammograms and radiologist workload reduction: a systematic review and meta-analysis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-06.