Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
J Biophotonics. 2018 Sep;11(9):e201800101. doi: 10.1002/jbio.201800101. Epub 2018 May 11.
Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.
准确和即时的疟疾诊断对于传染病的治疗至关重要。传统的疟疾诊断方法既费时又依赖专家的技能。因此，对于缺乏训练有素的显微镜专家的发展中国家的医疗保健来说，自动和简单的诊断方式是必不可少的。在本研究中，提出了一种新的自动传感方法，使用数字线内全息显微镜（DIHM）结合机器学习算法，以灵敏地检测未染色的疟疾感染的红细胞（iRBC）。为了识别 RBC 的特征，从单个 RBC 的分割全息图中提取了 13 个描述符。在 13 个描述符中，健康 RBC（hRBC）和 iRBC 之间有 10 个特征在统计学上有显著差异。应用了 6 种机器学习算法来有效地组合主导特征，并极大地提高了本方法的诊断能力。在 6 种测试算法训练的分类模型中，支持向量机（SVM）训练的模型在训练集（n=280，96.78%）和测试集（n=120，97.50%）中分离 hRBC 和 iRBC 的效果最好。这种基于 DIHM 的人工智能方法简单，不需要血液染色。因此，它将有益于疟疾的诊断。
