ArticleBiomedical optics express2020
Deep learning-based cell identification and disease diagnosis using spatio-temporal cellular dynamics in compact digital holographic microscopy.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- AI-driven dual-mode phase and label-free fluorescence imaging platform using a single-shot gabor hologram.Microsystems & nanoengineering · 2026Article
- AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms.Biomedical optics express · 2026Review
- Label-free detection of ovarian cancer cells in ascites-related cell models using digital holographic flow cytometry.Biomedical optics express · 2026Article
- Virtual Staining-Enabled Colorectal Cancer Metastasis Detection in Liquid Cytology Based on Digital Holography.Sensors (Basel, Switzerland) · 2025Article
- Diffraction-informed deep learning for molecular-specific holograms of breast cancer cells.APL bioengineering · 2025Article
- Spatial microfluidic holographic integrated platform for label-free and high-dimensional analysis of cancer heterogeneity.Nature communications · 2025Article
- Measurement of HbA1c with a new phase function using RBC phase image from a low-cost digital holographic microscopy.Biotechnology letters · 2025Article
- Characterization of a Single-Capture Bright-Field and Off-Axis Digital Holographic Microscope for Biological Applications.Sensors (Basel, Switzerland) · 2025Article
- Automated fast label-free quantification of cardiomyocyte dynamics with raw holograms for cardiotoxicity screening.Biomedical optics express · 2025Article
- Quantitative phase image stitching guided by reconstructed intensity images in one-shot double field of view multiplexed digital holographic microscopy.Biomedical optics express · 2024Article
- Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part I.Journal of biomedical optics · 2024Review
- Article
- Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis.Biomedical optics express · 2024Article
- A dataset of digital holograms of normal and thalassemic cells.Scientific data · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning.Nature biomedical engineering · 2023Article
- Synchronous Phase-Shifting Interference for High Precision Phase Imaging of Objects Using Common Optics.Sensors (Basel, Switzerland) · 2023Article
- Multi-Illumination Single-Holographic-Exposure Lensless Fresnel (MISHELF) Microscopy: Principles and Biomedical Applications.Sensors (Basel, Switzerland) · 2023Review
- SHAPR predicts 3D cell shapes from 2D microscopic images.iScience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We demonstrate a successful deep learning strategy for cell identification and disease diagnosis using spatio-temporal cell information recorded by a digital holographic microscopy system. Shearing digital holographic microscopy is employed using a low-cost, compact, field-portable and 3D-printed microscopy system to record video-rate data of live biological cells with nanometer sensitivity in terms of axial membrane fluctuations, then features are extracted from the reconstructed phase profiles of segmented cells at each time instance for classification. The time-varying data of each extracted feature is input into a recurrent bi-directional long short-term memory (Bi-LSTM) network which learns to classify cells based on their time-varying behavior. Our approach is presented for cell identification between the morphologically similar cases of cow and horse red blood cells. Furthermore, the proposed deep learning strategy is demonstrated as having improved performance over conventional machine learning approaches on a clinically relevant dataset of human red blood cells from healthy individuals and those with sickle cell disease. The results are presented at both the cell and patient levels. To the best of our knowledge, this is the first report of deep learning for spatio-temporal-based cell identification and disease detection using a digital holographic microscopy system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.