Kyeremah Charlotte, Paul Aditya S, Haehn Daniel, Duraisingh Manoj T, Yelleswarapu Chandra S
Department of Physics, University of Massachusetts Boston, 100 Morrissey Blvd, Boston, MA, 02125, USA.
Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Sci Rep. 2025 Aug 21;15(1):30733. doi: 10.1038/s41598-025-12899-3.
Digital holographic microscopy (DHM) has emerged as a powerful, label-free technique for visualizing and analyzing biological samples. By extracting the intrinsic optical properties of red blood cells (RBCs), DHM enables the detection of infection-induced morphological and biophysical changes. Traditional classification methods often rely on feature-specific analysis, which can lead to misclassification when a single parameter fails to differentiate between uninfected and infected cells. In this study, we present a novel features-based approach that integrates multiple features to classify Plasmodium falciparum-infected RBCs obtained using lensless inline DHM. Our analysis shows that phase-based features classification provides a more reliable indicator of infected RBCs compared to morphological features. Additionally, our features-based approach outperforms feature-specific methods that rely on individual attributes. The parasitemia detection rate improved from 48% (feature-specific method) to 61% (phase-based features method) on the same sample set, demonstrating enhanced detection accuracy. Furthermore, the proposed method achieved high specificity (98-100%), ensuring reliable identification of uninfected cells. Although our method slightly underestimates the parasitemia detection rate compared to Giemsa staining (90%), it offers a significant advantage as a real-time, label-free imaging tool, presenting a promising avenue for rapid and automated malaria diagnosis.
数字全息显微镜（DHM）已成为一种用于可视化和分析生物样本的强大的无标记技术。通过提取红细胞（RBC）的固有光学特性，DHM能够检测感染引起的形态和生物物理变化。传统的分类方法通常依赖于特定特征分析，当单个参数无法区分未感染和感染细胞时，可能会导致错误分类。在本研究中，我们提出了一种基于特征的新方法，该方法整合多个特征以对使用无透镜同轴DHM获得的恶性疟原虫感染的红细胞进行分类。我们的分析表明，与形态特征相比，基于相位的特征分类为感染的红细胞提供了更可靠的指标。此外，我们基于特征的方法优于依赖单个属性的特定特征方法。在同一样本集上，疟原虫血症检测率从48%（特定特征方法）提高到61%（基于相位的特征方法），证明了检测准确性的提高。此外，所提出的方法具有高特异性（98 - 100%），确保了未感染细胞的可靠识别。尽管与吉姆萨染色（90%）相比，我们的方法略微低估了疟原虫血症检测率，但它作为一种实时、无标记的成像工具具有显著优势，为快速和自动化疟疾诊断提供了一条有前景的途径。
