Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
J Med Screen. 2021 Dec;28(4):448-455. doi: 10.1177/0969141321998718. Epub 2021 Mar 9.
To determine the agreement between artificial intelligence software (AI) and radiographers in assessing breast positioning criteria for mammograms from standard digital mammography and digital breast tomosynthesis.
Assessment of breast positioning was performed by AI and by four radiographers in pairs of two on 156 examinations of women screened in Bergen, April to September 2019, as part of BreastScreen Norway. Ten criteria were used; three for craniocaudal and seven for mediolateral-oblique view. The criteria evaluated the appearance of the nipple, breast rotation, pectoral muscle, inframammary fold and pectoral nipple line. Intraclass correlation and Cohen's kappa coefficient (κ) were used to investigate the correlation and agreement between the radiographer's assessments and AI.
The intraclass correlation for the pectoral nipple line between the radiographers and AI was >0.92. A substantial to almost perfect agreement (κ > 0.69) was observed between the radiographers and AI on the nipple in profile criterion. We observed a slight to moderate agreement for the other criteria (κ = 0.06-0.52) and generally a higher agreement between the two pairs of radiographers (mean κ = 0.70) than between the radiographers and AI (mean κ = 0.41).
AI has great potential in evaluating breast position criteria in mammography by reducing subjectivity. However, varying agreement between radiographers and AI was observed. Standardized and evidence-based criteria for definitions, understandings and assessment methods are needed to reach optimal image quality in mammography.
评估人工智能软件（AI）与放射技师在评估标准数字化乳腺摄影和数字乳腺断层合成摄影的乳腺定位标准方面的一致性。
2019 年 4 月至 9 月，在卑尔根进行的 BreastScreen Norway 女性筛查中，对 156 例检查进行了 AI 和四位放射技师两两配对的乳腺定位评估。使用了 10 个标准；3 个用于头尾位，7 个用于内外斜位。评估的标准包括乳头外观、乳房旋转、胸大肌、乳房下皱襞和胸乳头线。采用组内相关系数和 Cohen's kappa 系数（κ）来研究放射技师评估和 AI 之间的相关性和一致性。
放射技师和 AI 之间的胸乳头线的组内相关系数>0.92。在侧位乳头标准中，放射技师和 AI 之间观察到高度一致到几乎完美的一致性（κ>0.69）。对于其他标准，我们观察到轻微到中度的一致性（κ=0.06-0.52），并且通常两个放射技师之间的一致性更高（平均κ=0.70），而不是放射技师和 AI 之间的一致性（平均κ=0.41）。
AI 在减少主观性方面在评估乳腺摄影中的乳腺定位标准方面具有巨大潜力。然而，我们观察到放射技师和 AI 之间存在不一致的情况。需要制定标准化和基于证据的定义、理解和评估方法标准，以达到乳腺摄影的最佳图像质量。
