Hehr Matthias, Sadafi Ario, Matek Christian, Lienemann Peter, Pohlkamp Christian, Haferlach Torsten, Spiekermann Karsten, Marr Carsten
Institute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
PLOS Digit Health. 2023 Mar 15;2(3):e0000187. doi: 10.1371/journal.pdig.0000187. eCollection 2023 Mar.
Explainable AI is deemed essential for clinical applications as it allows rationalizing model predictions, helping to build trust between clinicians and automated decision support tools. We developed an inherently explainable AI model for the classification of acute myeloid leukemia subtypes from blood smears and found that high-attention cells identified by the model coincide with those labeled as diagnostically relevant by human experts. Based on over 80,000 single white blood cell images from digitized blood smears of 129 patients diagnosed with one of four WHO-defined genetic AML subtypes and 60 healthy controls, we trained SCEMILA, a single-cell based explainable multiple instance learning algorithm. SCEMILA could perfectly discriminate between AML patients and healthy controls and detected the APL subtype with an F1 score of 0.86±0.05 (mean±s.d., 5-fold cross-validation). Analyzing a novel multi-attention module, we confirmed that our algorithm focused with high concordance on the same AML-specific cells as human experts do. Applied to classify single cells, it is able to highlight subtype specific cells and deconvolve the composition of a patient's blood smear without the need of single-cell annotation of the training data. Our large AML genetic subtype dataset is publicly available, and an interactive online tool facilitates the exploration of data and predictions. SCEMILA enables a comparison of algorithmic and expert decision criteria and can present a detailed analysis of individual patient data, paving the way to deploy AI in the routine diagnostics for identifying hematopoietic neoplasms.
可解释人工智能被认为对临床应用至关重要，因为它能使模型预测合理化，有助于建立临床医生与自动化决策支持工具之间的信任。我们开发了一种内在可解释的人工智能模型，用于从血液涂片对急性髓系白血病亚型进行分类，发现该模型识别出的高关注度细胞与人类专家标记为具有诊断相关性的细胞一致。基于来自129例被诊断为世界卫生组织定义的四种遗传急性髓系白血病亚型之一的患者以及60名健康对照的数字化血液涂片的80000多张单个白细胞图像，我们训练了SCEMILA，一种基于单细胞的可解释多实例学习算法。SCEMILA能够完美地区分急性髓系白血病患者和健康对照，并以0.86±0.05（平均值±标准差，5折交叉验证）的F1分数检测出急性早幼粒细胞白血病亚型。通过分析一个新颖的多注意力模块，我们证实我们的算法与人类专家高度一致地聚焦于相同的急性髓系白血病特异性细胞。应用于对单个细胞进行分类时，它能够突出亚型特异性细胞，并对患者血液涂片的组成进行解卷积，而无需对训练数据进行单细胞注释。我们庞大的急性髓系白血病遗传亚型数据集已公开可用，一个交互式在线工具便于对数据和预测进行探索。SCEMILA能够比较算法和专家决策标准，并能对个体患者数据进行详细分析，为在常规诊断中部署人工智能以识别造血肿瘤铺平了道路。
