■稀薄的血液涂片的注释图像数据集的稀缺性使得疟原虫物种之间的专家级区分具有挑战性。这里,我们的目的是建立一种深度学习算法,用于在薄血涂片中识别和分类疟疾寄生虫,并评估其性能和临床前景。
■您只看一次v7被用作训练人工智能算法模型的骨干网络。训练,验证,随机选择每个疟疾寄生虫类别的测试集。对12546例疟疾寄生虫的不同感染阶段的12708例薄血涂片图像进行了综合分析,包括恶性疟原虫,间日疟原虫,疟疾P,POvale,Pknowlesi,和食蟹猴。从380名诊断为疟疾的患者获得外周血样本。此外,来自被诊断为疟疾的猴子的血液样本被用于分析食蟹猴。通过各种评估指标评估检测疟原虫感染的血细胞的准确性。
■识别1116种疟疾寄生虫的总时间为13秒,测试集中每个寄生虫的平均分析时间为0.01秒。平均准确率为0.902,感染红细胞的召回率和准确率分别为96.0%和94.9%,分别。敏感性和特异性超过96.8%和99.3%,接收器工作特性曲线下的面积>0.999。对于滋养体和裂殖体,观察到最高的灵敏度(97.8%)和特异性(99.8%)。
■该算法可以帮助促进疟疾寄生虫的临床和形态检查。
UNASSIGNED: Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among Plasmodium species challenging. Here, we aimed to establish a deep learning algorithm for identifying and classifying malaria parasites in thin blood smears and evaluate its performance and clinical prospect.
UNASSIGNED: You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including P falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi. Peripheral blood samples were obtained from 380 patients diagnosed with malaria. Additionally, blood samples from monkeys diagnosed with malaria were used to analyze P cynomolgi. The accuracy for detecting Plasmodium-infected blood cells was assessed through various evaluation metrics.
UNASSIGNED: The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve >0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and merozoites.
UNASSIGNED: The algorithm can help facilitate the clinical and morphologic examination of malaria parasites.