BACKGROUND:Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by measuring the deformability of RBCs from laser diffraction patterns under varying shear stress. In addition to estimations from model comparisons, use of maximum Elongation Index differences (ΔEImax) at different laser intensity levels was recently proposed for the estimation of SC fractions. OBJECTIVE:Implement a convolutional neural network to accurately estimate rigid-cell fraction and RBC concentration from laser diffraction patterns without using a theoretical model and eliminating the ektacytometer dependency for deformability measurements. METHODS:RBCs were collected from control patients. Rigid-cell fraction experiments were performed using varying concentrations of glutaraldehyde. Serial dilutions were used for varying the concentration of RBC. A convolutional neural network was constructed using Python and TensorFlow. RESULTS:Our measurements and model predictions show that a linear relationship between ΔEImax and rigid-cell fraction exists only for rigid-cell fractions less than 0.2. Our proposed neural network architecture can be used successfully for both RBC concentration and rigid-cell fraction estimations without a need for a theoretical model.

中文翻译：

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深度学习在红细胞变形能力评估中的应用

背景：异常红细胞 (RBC) 变形能力的测量是镰状细胞性贫血 (SCA) 的主要指标，需要标准化的量化方法。Ektacytometry 通常用于通过测量不同剪切应力下激光衍射图案中红细胞的变形能力来估计镰状细胞 (SC) 的分数。除了来自模型比较的估计之外，最近还建议使用不同激光强度水平下的最大伸长指数差异 (ΔEImax) 来估计 SC 分数。目标：实施卷积神经网络，从激光衍射图案准确估计刚性细胞分数和 RBC 浓度，无需使用理论模型，也无需消除 ektacytometer 对变形性测量的依赖性。方法：从对照患者中收集红细胞。使用不同浓度的戊二醛进行刚性细胞部分实验。连续稀释用于改变 RBC 的浓度。使用 Python 和 TensorFlow 构建了一个卷积神经网络。结果：我们的测量和模型预测表明，ΔEImax 和刚性细胞分数之间的线性关系仅存在于小于 0.2 的刚性细胞分数。我们提出的神经网络架构可成功用于 RBC 浓度和刚性细胞分数估计，而无需理论模型。我们的测量和模型预测表明，ΔEImax 和刚性细胞分数之间的线性关系仅存在于小于 0.2 的刚性细胞分数。我们提出的神经网络架构可成功用于 RBC 浓度和刚性细胞分数估计，而无需理论模型。我们的测量和模型预测表明，ΔEImax 和刚性细胞分数之间的线性关系仅存在于小于 0.2 的刚性细胞分数。我们提出的神经网络架构可成功用于 RBC 浓度和刚性细胞分数估计，而无需理论模型。