Visualization of the Nipple in Profile: Does It Really Affect Selected Outcomes in Organized Mammographic Screening?

Impact of Artificial Intelligence-driven Quality Improvement Software on Mammography Technical Repeat and Recall Rates

Poor positioning decreases mammography sensitivity and is arguably the single most important contributor to image quality (IQ). Inadequate IQ may subject patients to technical repeat views during the examination or return for technical recalls. Artificial intelligence (AI) software can objectively evaluate breast positioning and compression metrics for all images and technologists. This study assessed whether implementation of AI software across the authors' institution improved IQ and reduced rates of technical repeats and recalls (TR). From April 2019 to March 2022, TR was retrospectively evaluated for 40 technologists (198 054 images; Centricity electronic medical record system, GE HealthCare), and AI IQ metrics were available for 42 technologists (211 821 images; Analytics, Volpara Health Technologies). Diagnostic and digital breast tomosynthesis images and implant cases were excluded. Kolmogorov-Smirnov, χ2, and paired t tests were used to evaluate whether AI IQ metrics and TR rates improved between the initial and most recent 12-month periods following AI software implementation (ie, baseline [April 2019 to March 2020] vs current [April 2021 to March 2022]). Comparing baseline with current periods, TR significantly reduced from 0.77% (788 of 102 953 images) to 0.17% (160 of 95 101 images), respectively (P < .001), and overall mean quality score improved by 6% ([2.42 - 2.28]/2.28; P = .001), demonstrating the potential of AI software to improve IQ and reduce patient TR. Keywords: Mammography, Breast, Oncology, QA/QC, Screening, Technology Assessment © RSNA, 2023.