Background Clinical babesiosis is diagnosed, and parasite burden is determined, by microscopic inspection of a thick or thin Giemsa-stained peripheral blood smear. However, quantitative analysis by manual microscopy is subject to error. As such, methods for the automated measurement of percent parasitemia in digital microscopic images of peripheral blood smears could improve clinical accuracy, relative to the predicate method. Methods Individual erythrocyte images were manually labeled as “parasite” or “normal” and were used to train a model for binary image classification. The best model was then used to calculate percent parasitemia from a clinical validation dataset, and values were compared to a clinical reference value. Lastly, model interpretability was examined using an integrated gradient to identify pixels most likely to influence classification decisions. Results The precision and recall of the model during development testing were 0.92 and 1.00, respectively. In clinical validation, the model returned increasing positive signal with increasing mean reference value. However, there were 2 highly erroneous false positive values returned by the model. Further, the model incorrectly assessed 3 cases well above the clinical threshold of 10%. The integrated gradient suggested potential sources of false positives including rouleaux formations, cell boundaries, and precipitate as deterministic factors in negative erythrocyte images. Conclusions While the model demonstrated highly accurate single cell classification and correctly assessed most slides, several false positives were highly incorrect. This project highlights the need for integrated testing of machine learning-based models, even when models in the development phase perform well.

中文翻译：

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数字显微镜和密集连接的卷积神经网络在巴贝虫感染红细胞自动定量中的应用

背景 临床巴贝虫病的诊断和寄生虫负荷的确定是通过显微镜检查厚或薄的吉姆萨染色外周血涂片。然而，手动显微镜的定量分析容易出错。因此，相对于谓词方法，自动测量外周血涂片数字显微图像中寄生虫血症百分比的方法可以提高临床准确性。方法 将单个红细胞图像手动标记为“寄生虫”或“正常”，并用于训练二值图像分类模型。然后使用最佳模型从临床验证数据集中计算寄生虫血症百分比，并将值与临床参考值进行比较。最后，使用集成梯度检查模型的可解释性，以识别最有可能影响分类决策的像素。结果模型在开发测试中的准确率和召回率分别为0.92和1.00。在临床验证中，模型返回的阳性信号随着平均参考值的增加而增加。但是，模型返回了 2 个高度错误的误报值。此外，该模型错误地评估了 3 例远高于 10% 的临床阈值的病例。综合梯度表明了潜在的假阳性来源，包括轮盘形成、细胞边界和沉淀物作为阴性红细胞图像中的确定性因素。结论 虽然该模型展示了高度准确的单细胞分类并正确评估了大多数载玻片，几个误报是非常不正确的。该项目强调了对基于机器学习的模型进行集成测试的必要性，即使在开发阶段的模型表现良好。