Background and Objectives: Malaria is a disease that can result in a variety of complications. Diagnosis is carried out by an optical microscope and depends on operator experience. The use of artificial intelligence to identify morphological patterns in erythrocytes would improve our diagnostic capability. The object of this study was therefore to establish computer viewing models able to classify blood cells infected with Plasmodium spp. to support malaria diagnosis by optical microscope. Materials and Methods: A total of 27,558 images of human blood sample extensions were obtained from a public data bank for analysis; half were of parasite-infected red cells (n = 13,779), and the other half were of uninfected erythrocytes (n = 13,779). Six models (five machine learning algorithms and one pre-trained for a convolutional neural network) were assessed, and the performance of each was measured using metrics like accuracy (A), precision (P), recall, F1 score, and area under the curve (AUC). Results: The model with the best performance was VGG-19, with an AUC of 98%, accuracy of 93%, precision of 92%, recall of 94%, and F1 score of 93%. Conclusions: Based on the results, we propose a convolutional neural network model (VGG-19) for malaria diagnosis that can be applied in low-complexity laboratories thanks to its ease of implementation and high predictive performance.

Keywords: artificial intelligence; blood morphology; convolutional neural network; diagnosis; laboratory medicine; machine learning; malaria; precision medicine.

背景和目的：疟疾是一种可能引发各种并发症的疾病。诊断通常通过光学显微镜进行，且依赖于操作者的经验。使用人工智能来识别红细胞中的形态模式可以提高我们的诊断能力。因此，本研究的目标是建立能够区分感染了Plasmodium物种的血细胞的计算机观察模型，以支持光学显微镜下的疟疾诊断。材料和方法：从公共数据库中获取了27,558张人类血液样本延展图像进行分析；其中一半是寄生虫感染的红细胞（n=13,779），另一半是没有被感染的红细胞（n=13,779）。评估了六种模型（五种机器学习算法和一种预训练的卷积神经网络），并通过准确性（A）、精确度（P）、召回率、F1评分和曲线下面积（AUC）等指标衡量每个模型的表现。结果：表现最佳的模型是VGG-19，其AUC为98%，准确率为93%，精确度为92%，召回率为94%，F1评分为93%。结论：根据研究结果，我们提出了一种卷积神经网络模型（VGG-19），可以应用于低复杂度的实验室环境中，因为它易于实施且具有较高的预测性能。

关键词：人工智能；血液形态学；卷积神经网络；诊断；临床检验医学；机器学习；疟疾；精准医疗。