目的 研究并建立基于人工智能的疟原虫辅助检测平台，评价其应用效果。 方法 收集2008—2022年武汉市境外输入性疟疾现症病例的血涂片，采用智能检测平台的自动图像采集系统获取镜下图像，由专业镜检人员进行标注，创建疟原虫薄血膜图像数据集。通过训练检测模型进行疟原虫、白细胞和红细胞的检测，推理出阴阳性、感染率、感染虫种，并展示于应用界面中。采用真阳性、真阴性、假阳性、假阴性的样本数量，计算准确率、精确率、召回率、F1分数来评估模型的性能。参照疟疾镜检人员的技术考核方式，模拟镜检过程，取10张血涂片对模型检测综合结果进行评分。 结果 薄血膜图像数据集共包含3 704张图像，由专家使用边界框进行注释，注释包含7 835个虫体和786个白细胞标注。经YOLOv8模型混淆矩阵分析，模型整体正确率为85.5%、精确率为98.5%、召回率为93.3%、F1分数为95.8%、平均精度@0.50分数为96.2%。间日疟原虫和三日疟原虫的检测精确率较高，分别为96.9%和93.8%。恶性疟原虫和白细胞的检测精确率较低，分别为87.2%和87.8%。除恶性疟原虫的F1分数为89.7%外，其他类别检测的F1分数均高于90.0%。召回率由高到低为三日疟原虫（94.9%）、白细胞（94.2%）、间日疟原虫（94.1%）、卵形疟原虫（93.6%）、恶性疟原虫（92.4%）。共有64个目标被误检，误检率为6.7%。平台与专家检测结果正确率均为100%，平台得分90，无阴阳性误判现象，但鉴别虫种存在误检。 结论 本人工智能检测平台能较好地识别定位疟原虫、白细胞及红细胞，评分结果趋近于镜检专家，具备较高的检测能力，可应用于临床疟原虫镜检的辅助诊断。

Objective To develop and establish an artificial intelligence (AI) platform assisting malaria parasites detection, and the applied effect was evaluated. Methods Blood smears were collected from current cases of overseas imported malaria in Wuhan City from 2008 to 2022. Microscopic images were captured with an automatic imaging collection system, and annotated by professional microscopists to create a dataset of thin blood smear images. A detection model was employed to detect Plasmodium, white blood cells (WBCs) and red blood cells (RBCs) to deduce negativity, positivity, infection rate and infected parasite species, and the results were displayed in the application interface. Based on numbers of true positive, true negative, false positive and false negative samples, the accuracy, precision, recall, and F1 score were estimated to evaluate the performance of the model. Microscopic examination procedures were mimicked according to the technical assessment method of malaria microscopists, and 10 blood smears were randomly selected to score the model comprehensive results. Results The thin blood smears image datasets included 3 704 images that were annotated by microscopist, including 7 835 Plasmodium annotations and 786 WBC annotations. Results from the YOLOv8 confusion matrix showed an overall accuracy of 85.5%, precision of 98.5%, recall of 93.3%, F1 score of 95.8%, and an mAP@0.50 score of 96.2%. The model had a high accuracy for detection of P. vivax (96.9%) and P. malariae (93.8%), and a low accuracy for detection of P. falciparum (87.2%) and WBCs (87.8%). Except an 89.7% F1 score for detection of P. falciparum, the model had F1 scores of over 90.0% for detection of other Plasmodium species and WBCs. The recall of the model for detection of 4 Plasmodium species and WBCs, was the highest recall for detection of P. malariae (94.9%), followed by WBC (94.2%), P. vivax (94.1%), P. ovale (93.6%) and P. falciparum (92.4%). A total of 64 targets were falsely identified, with a false detection rate of 6.7%. The correction rates of the platform and professional microscopist were both 100%, and the clinical microscopy score was 90 points, with no false positives or negatives identified; however, there were false detections in identification of malaria parasite species. Conclusion The platform is effective to identify and locate malaria parasites, WBCs and RBCs, and its scoring results are approaching to the microscopist. Due to a high detection capability, the model is feasible for auxiliary diagnosis of clinical microscopy of malaria parasites.