Fairness in AI: are deep learning-based CMR segmentation algorithms biased?
Fairness in AI: are deep learning-based CMR segmentation algorithms biased?
复制标题
AI 公平性：基于深度学习的 CMR 分割算法是否存在偏见？
DOI：
10.1093/eurheartj/ehab724.3055
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发表时间：
2021
影响因子：
39.3
通讯作者：
Puyol Anton E
中科院分区：
文献类型：
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作者：
Puyol Anton E
背景/简介人工智能（AI）正在为心血管医学的变革提供机会。人工智能技术应用中的一个特殊挑战是其潜在的内在和外在偏见，例如基于性别和/或种族的偏见。除非得到令人满意的解决，否则这些偏见可能导致早期诊断、治疗和结果的不平等。人工智能公平性是一个相对较新的但发展迅速的研究领域，它涉及评估和解决人工智能模型中潜在的偏见。目的在大型数据库中对基于人工智能的心脏MR分割模型的偏差进行首次分析。方法采用基于深度学习（DL）的分割网络“nnU-Net”框架[1]，对全心脏周期内的短轴心脏MR图像进行心室和心肌的自动分割。使用的数据集包括5903名受试者（61.5±7.1岁）的舒张末期和收缩末期短轴心脏MR图像。使用5倍交叉验证（分割：训练60% /验证20% /测试20%）对nnU-Net网络进行训练和评估。种族和性别数据来自UK Biobank数据库，其分布情况如图1所示。为了评估分割网络中的性别和种族偏见，我们比较了Dice分数（衡量手动和自动分割之间的重叠）和按种族和性别分组的患者双心室体积和功能测量的绝对误差。结果图2显示了Dice分数以及整个数据库的体积和功能测量，按性别和种族分层。总体人群的结果显示，人工和自动分割之间的一致性非常好，这与先前报道的结果一致[2-3]。然而，我们发现不同种族之间的Dice得分和体积测量在统计上存在显著差异，这表明分割网络对少数种族群体有偏见。性别间在Dice得分上没有显著差异。同样，对于舒张末期、收缩期末期容积和射血分数，除白人外，总体人群和所有种族之间的绝对误差在统计学上有显著差异。结论：我们首次表明，基于dl的心脏MR分割模型存在种族偏见。我们的假设是，这种偏见是训练数据不平衡的结果，结果表明，当使用英国生物银行数据库进行训练时，存在种族偏见，但没有性别偏见，这是性别平衡而不是种族平衡的。在这项工作中，我们希望强调基于dl的图像分割模型在转化为临床环境时可能存在的偏见问题。资金来源类型：公共拨款-仅限国家预算。主要资金来源：- EPSRC-伦敦国王学院生物医学工程与成像科学学院Wellcome EPSRC医学工程中心
Background/IntroductionArtificial intelligence (AI) is providing opportunities to transform cardiovascular medicine. A particular challenge in the application of AI technology is their potential for intrinsic and extrinsic biases, such as those based on gender and/or ethnicity. Unless satisfactorily addressed, these biases could lead to inequalities in early diagnosis, treatments and outcomes. Fairness in AI is a relatively new but fast-growing research field which deals with assessing and addressing potential bias in AI models.PurposeTo perform the first analysis that assesses bias in AI-based cardiac MR segmentation models in a large-scale database.MethodsA state-of-the-art deep learning (DL) based segmentation network, the “nnU-Net” framework [1], was used for automatic segmentation of both ventricles and the myocardium from cine short-axis cardiac MR over the full cardiac cycle. The dataset used consisted of end-diastole and end-systole short-axis cine cardiac MR images of 5,903 subjects (61.5±7.1 years). The nnU-Net network was trained and evaluated using a 5-fold cross validation (splits: train 60% / validation 20% / test 20%). Data on race and gender were obtained from the UK Biobank database and their distribution is summarized in Figure 1. To assess gender and racial bias in the segmentation network, we compared the Dice scores - which measure the overlap between manual and automatic segmentations – and the absolute error in measurements of biventricular volumes and function between patients grouped by ethnicity and gender.ResultsFigure 2 shows the Dice scores and the volumetric and functional measures for the full database, stratified by gender and by ethnicity. Results on the overall population showed an excellent agreement between the manual and automatic segmentations which is consistent with previous reported results [2–3]. However, we find statistically significant differences in Dice scores as well as volumetric measures between different ethnicities, showing that the segmentation network is biased against minority racial groups. No significant differences were found in Dice scores between genders. Similarly, for the end diastolic, end systolic volumes and ejection fraction, there were statistically significant differences in absolute error between the overall population and all racial groups except white.Conclusion(s)We have shown, for the first time, that racial bias exists in DL-based cardiac MR segmentation models. Our hypothesis is that this bias is a result of the unbalanced nature of the training data, and this is supported by the results which show that there is racial bias but not gender bias when trained using the UK Biobank database, which is gender-balanced but not race-balanced. In this work we want to highlight the potential issue of bias in DL-based image segmentation models when translating into a clinical environment.Funding AcknowledgementType of funding sources: Public grant(s) – National budget only. Main funding source(s): - EPSRC- Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King's College LondonFigure 1Figure 2