Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis

Abstract

Hematologists evaluate alterations in blood cell enumeration and morphology to confirm peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose using smartphone-based autofluorescence microscopy (Smart-AM) for imaging label-free blood smears at subcellular resolution with automatic hematological analysis. Smart-AM enables rapid and label-free visualization of morphological features of normal and abnormal blood cells (including leukocytes, erythrocytes, and thrombocytes). Moreover, assisted with deep-learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images which show clear cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.