Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis.
Biomed Opt Express
; 15(4): 2636-2651, 2024 Apr 01.
Article
in En
| MEDLINE
| ID: mdl-38633093
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Biomed Opt Express
/
Biomedical optics express
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Estados Unidos