In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 6 ( 2023-6-27), p. e0279525-
Abstract:
In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and time-consuming. Studies have shown promising leukocyte identification performance on blood fractions, using third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. Objective To extend leukocyte differentiation to BALF samples using THG/MPEF microscopy, and to show the potential of a trained deep learning algorithm for automated leukocyte identification and quantification. Methods Leukocytes from blood obtained from three healthy individuals and one asthma patient, and BALF samples from six ILD patients were isolated and imaged using label-free microscopy. The cytological characteristics of leukocytes, including neutrophils, eosinophils, lymphocytes, and macrophages, in terms of cellular and nuclear morphology, and THG and MPEF signal intensity, were determined. A deep learning model was trained on 2D images and used to estimate the leukocyte ratios at the image-level using the differential cell counts obtained using standard cytological techniques as reference. Results Different leukocyte populations were identified in BALF samples using label-free microscopy, showing distinctive cytological characteristics. Based on the THG/MPEF images, the deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, reaching 〉 90% accuracy on BALF samples in the hold-out testing set. Conclusions Label-free THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios has potential to speed-up the diagnostic process and to reduce costs, workload and inter-observer variations.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0279525
DOI:
10.1371/journal.pone.0279525.g001
DOI:
10.1371/journal.pone.0279525.g002
DOI:
10.1371/journal.pone.0279525.g003
DOI:
10.1371/journal.pone.0279525.g004
DOI:
10.1371/journal.pone.0279525.g005
DOI:
10.1371/journal.pone.0279525.g006
DOI:
10.1371/journal.pone.0279525.g007
DOI:
10.1371/journal.pone.0279525.t001
DOI:
10.1371/journal.pone.0279525.t002
DOI:
10.1371/journal.pone.0279525.t003
DOI:
10.1371/journal.pone.0279525.s001
DOI:
10.1371/journal.pone.0279525.s002
DOI:
10.1371/journal.pone.0279525.r001
DOI:
10.1371/journal.pone.0279525.r002
DOI:
10.1371/journal.pone.0279525.r003
DOI:
10.1371/journal.pone.0279525.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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