Influence of Mammography Acquisition Parameters on AI and Radiologist Interpretive Performance

Purpose To evaluate the impact of screening mammography acquisition parameters on the interpretive performance of artificial intelligence (AI) and radiologists. Materials and Methods The associations between seven mammogram acquisition parameters-mammography machine version, kilovoltage peak, x-ray exposure delivered, relative x-ray exposure, paddle size, compression force, and breast thickness-and AI and radiologist performance in interpreting two-dimensional screening mammograms acquired by a diverse health system between December 2010 and 2019 were retrospectively evaluated. The top 11 AI models and the ensemble model from the Digital Mammography Dialogue on Reverse Engineering Assessment and Methods (DREAM) Challenge were assessed. The associations between each acquisition parameter and the sensitivity and specificity of the AI models and the radiologists' interpretations were separately evaluated using generalized estimating equations-based models at the examination level, adjusted for several clinical factors. Results The dataset included 28 278 screening two-dimensional mammograms from 22 626 women (mean age ± SD, 58.5 years ± 11.5; 4913 women had multiple mammograms). Of these, 324 examinations resulted in a breast cancer diagnosis within 1 year. The acquisition parameters were significantly associated with the performance of both AI and radiologists, with absolute effect sizes reaching 10% for sensitivity and 5% for specificity; however, the associations differed between AI and radiologists for several parameters. Increased exposure delivered reduced the specificity for the ensemble AI (-4.5% per 1 SD increase; P < .001) but not radiologists (P = .44). Increased compression force reduced the specificity for radiologists (-1.3% per 1 SD increase; P < .001) but not for AI (P = .60). Conclusion Screening mammography acquisition parameters impacted the performance of both AI and radiologists, with some parameters impacting performance differently. Keywords: AI Robustness, Mammography, Medical Physics Supplemental material is available for this article. © RSNA, 2025 See also commentary by Lee and Bae in this issue.