Machine learning‐based in‐line holographic sensing of unstained malaria‐infected red blood cells

• Published in: Journal of biophotonics Vol. 11; no. 9; pp. e201800101 - n/a
• Main Authors: Go, Taesik, Kim, Jun H., Byeon, Hyeokjun, Lee, Sang J.
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.09.2018; Wiley Subscription Services, Inc
• Subjects: Algorithms; Artificial intelligence; Developing countries; Diagnosis; Diagnostic systems; digital holographic microscopy; Erythrocytes; Health care; Holograms; Holography; Infectious diseases; LDCs; Learning algorithms; Machine learning; machine learning algorithm; Malaria; Microscopy; Support vector machines; Vector-borne diseases; diagnosis; digital holographic microscopy; malaria; machine learning algorithm
• ISSN: 1864-063X; 1864-0648
• DOI: 10.1002/jbio.201800101

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. Accurate diagnosis of malaria disease is crucial for avoiding malaria‐associated fatal deaths. Here, new sensitive detection modality of unstained malaria‐infected red blood cells (iRBCs) is developed. After acquiring of RBCs using digital in‐line holographic microscopy (DIHM) system, 10 valuable parameters containing information about morphological descriptors and light scattering characteristics were extracted for malaria diagnosis. Machine learning algorithms were applied to establish classification models and support vector machine (SVM) model discriminatesd the healthy RBCs and iRBCs with a high accuracy.