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Opt. Express"],"published-print":{"date-parts":[[2024,4,1]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1364\/boe.511384","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T13:00:06Z","timestamp":1706792406000},"page":"2636","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis"],"prefix":"10.1364","volume":"15","author":[{"given":"Bingxin","family":"Huang","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Kang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor T. 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