Authors
Chetna Patel, Shilpa Das, Mugdha Joglekar, Vismay Trivedi, Gyanendra Sheoran, Bahram Javidi, Arun Anand
Published in
Journal of the Optical Society of America. A, Optics, image science, and vision. Volume 43. Issue 8. Pages D1-D9. Aug 01, 2026.
Abstract
Digital holographic microscopy enables high-contrast, label-free quantitative phase imaging of transparent cells using visible light. This imaging modality can provide many biophysical and biomechanical cell features based on the numerically processed digital holograms. Sickle cell disease is a condition that affects red blood cells and is a significant health concern worldwide. Its clinical diagnosis, conventionally, is made using high-performance liquid chromatography, which is costly and time-consuming and requires experts to interpret the results. This paper presents a cost-effective method for screening sickle cell positive samples using a compact, self-referencing lateral shearing digital holographic microscope. Holographic videos of healthy and sickle cell positive samples are recorded using a digital holographic microscope and are numerically reconstructed. Both spatial and temporal features of the cells are extracted from the recorded holographic videos. The distribution of these features indicates the difference between healthy and sickle-cell-positive red blood cells. These features are used with classical machine learning classifiers to evaluate classification accuracy. The results are compared to determine the best machine learning model for label-free sickle cell identification. Results show that simple linear classifiers are sufficient due to the physical separability of the extracted features.
PMID:
42862980
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.
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