Artificial intelligence and consistency in patient care: a large-scale longitudinal study of mammographic density assessment.

BJR artificial intelligence Pub Date : 2025-03-03 eCollection Date: 2025-01-01 DOI:10.1093/bjrai/ubaf004
Susan O Holley, Daniel Cardoza, Thomas P Matthews, Elisha E Tibatemwa, Rodrigo Morales Hoil, Adetunji T Toriola, Aimilia Gastounioti
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Abstract

Objectives: To assess whether use of an artificial intelligence (AI) model for mammography could result in more longitudinally consistent breast density assessments compared with interpreting radiologists.

Methods: The AI model was evaluated retrospectively on a large mammography dataset including 50 sites across the United States from an outpatient radiology practice. Examinations were acquired on Hologic imaging systems between 2016 and 2021 and were interpreted by 39 radiologists (36% fellowship trained; years of experience: 2-37 years). Longitudinal patterns in 4-category breast density and binary breast density (non-dense vs. dense) were characterized for all women with at least 3 examinations (61 177 women; 214 158 examinations) as constant, descending, ascending, or bi-directional. Differences in longitudinal density patterns were assessed using paired proportion hypothesis testing.

Results: The AI model produced more constant (P < .001) and fewer bi-directional (P < .001) longitudinal density patterns compared to radiologists (AI: constant 81.0%, bi-directional 4.9%; radiologists: constant 56.8%, bi-directional 15.3%). The AI density model also produced more constant (P < .001) and fewer bi-directional (P < .001) longitudinal patterns for binary breast density. These findings held in various subset analyses, which minimize (1) change in breast density (post-menopausal women, women with stable image-based BMI), (2) inter-observer variability (same radiologist), and (3) variability by radiologist's training level (fellowship-trained radiologists).

Conclusions: AI produces more longitudinally consistent breast density assessments compared with interpreting radiologists.

Advances in knowledge: Our results extend the advantages of AI in breast density evaluation beyond automation and reproducibility, showing a potential path to improved longitudinal consistency and more consistent downstream care for screened women.

人工智能和患者护理的一致性:乳房x光密度评估的大规模纵向研究。
目的:评估使用人工智能(AI)模型进行乳房x光检查是否能比解释放射科医生获得更纵向一致的乳腺密度评估。方法:人工智能模型在一个大型乳房x线摄影数据集上进行回顾性评估,该数据集包括美国门诊放射学实践的50个地点。2016年至2021年期间在Hologic成像系统上获得检查结果,由39名放射科医生(36%接受过奖学金培训;工作年限:2-37年)。四类乳腺密度和二元乳腺密度(非致密与致密)的纵向模式在至少3次检查的所有妇女中被表征(61 177名妇女;214 158次考试)作为常数,下降,上升,或双向。采用配对比例假设检验评估纵向密度模式的差异。结果:与放射科医师相比,人工智能模型产生了更多的恒定(P < .001)和更少的双向(P < .001)纵向密度模式(人工智能:恒定81.0%,双向4.9%;放射科医师:固定56.8%,双向15.3%)。人工智能密度模型也产生了更多的恒定(P < .001)和更少的双向(P < .001)乳房密度纵向模式。这些发现在不同的子集分析中成立,这些分析最大限度地减少了(1)乳房密度的变化(绝经后妇女,基于图像的稳定BMI的妇女),(2)观察者之间的变异性(同一放射科医生),(3)放射科医生培训水平的变异性(研究员培训的放射科医生)。结论:与口译放射科医生相比,人工智能产生了更纵向一致的乳腺密度评估。知识的进步:我们的研究结果扩展了人工智能在乳腺密度评估中的优势,超越了自动化和可重复性,显示了改善纵向一致性的潜在途径,并为筛查妇女提供了更一致的下游护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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