Abstract：
In sub-Saharan Africa, acute-onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the spleen, leading to the presence of distinct red blood cells (RBCs) with altered morphological characteristics. We hypothesized that these RBCs could be detected systematically and at scale in peripheral blood films (PBFs) by harnessing the capabilities of deep learning models. Assessment of PBFs by a microscopist does not scale for this task and is subject to variability. Here we introduce a deep learning model, leveraging a weakly supervised Multiple Instance Learning framework, to Identify SMA (MILISMA) through the presence of morphologically changed RBCs. MILISMA achieved a classification accuracy of 83% (receiver operating characteristic area under the curve [AUC] of 87%; precision-recall AUC of 76%). More importantly, MILISMA\'s capabilities extend to identifying statistically significant morphological distinctions (p < 0.01) in RBCs descriptors. Our findings are enriched by visual analyses, which underscore the unique morphological features of SMA-affected RBCs when compared to non-SMA cells. This model aided detection and characterization of RBC alterations could enhance the understanding of SMA\'s pathology and refine SMA diagnostic and prognostic evaluation processes at scale.
摘要：
在撒哈拉以南非洲，急性发作的严重疟疾贫血（SMA）是一个关键的挑战，尤其影响五岁以下儿童。SMA中血细胞比容的急性下降被认为是由脾脏中吞噬的病理过程增加引起的。导致存在具有改变的形态学特征的不同的红细胞（RBC）。我们假设通过利用深度学习模型的能力，可以在外周血膜（PBF）中系统地大规模检测这些红细胞。显微镜对PBF的评估不能按比例进行此任务，并且会发生变化。这里我们介绍一个深度学习模型,利用弱监督多实例学习框架，通过形态学改变的红细胞的存在来识别SMA(MILISMA)。MILISMA的分类准确率为83％（曲线下的接受者工作特征面积[AUC]为87％；精确召回AUC为76％）。更重要的是,MILISMA的能力扩展到识别红细胞描述符中具有统计学意义的形态学差异（p<0.01）。视觉分析丰富了我们的发现，这强调了与非SMA细胞相比，受SMA影响的红细胞的独特形态特征。该模型辅助RBC改变的检测和表征可以增强对SMA病理学的理解，并细化SMA诊断和预后评估过程。
