ArticleScientific reports2020
High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Artificial Intelligence in the choice of sperm for ICSI - A Literature Review.JBRA assisted reproduction · 2026Pooled it
- Utilization of artificial intelligence in Men's Health: Opportunities for innovation and quality improvement.International journal of impotence research · 2026Review
- Real-time snapping dynamics and nanoscale thickness profiling of salmon keratocyte tunneling nanotubes using partially coherent quantitative phase microscopy.Scientific reports · 2026Article
- Label-free correlative morpho-chemical tomography of 3D kidney mesangial cells.Journal of biomedical optics · 2026Article
- A Novel Lightweight Deep Learning Model for Boar Sperm Head Detection in Microscopic Images: YOLO11_SRP.Animals : an open access journal from MDPI · 2026Article
- Deep learning-enabled morphology analysis of bovine sperm for label-free imaging flow cytometry.Frontiers in veterinary science · 2026Article
- Quantitative holographic analysis in stallion spermatozoa following cryopreservation.Scientific reports · 2025Article
- Spatial microfluidic holographic integrated platform for label-free and high-dimensional analysis of cancer heterogeneity.Nature communications · 2025Article
- [Advances in the Application of Artificial Intelligence in the Field of Male Infertility].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Review
- Multidimensional morphological analysis of live sperm based on multiple-target tracking.Computational and structural biotechnology journal · 2024Article
- A narrative review of sperm selection technology for assisted reproduction techniques.Translational andrology and urology · 2024Review
- Label-free, high-throughput holographic imaging to evaluate mammalian gametes and embryos†.Biology of reproduction · 2024Review
- High space-time bandwidth product imaging in low coherence quantitative phase microscopy.Scientific reports · 2024Article
- Roadmap on computational methods in optical imaging and holography [invited].Applied physics. B, Lasers and optics · 2024Review
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Recent advancements to increase success in assisted reproductive technologies in cattle.Animal reproduction · 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
- Highly sensitive quantitative phase microscopy and deep learning aided with whole genome sequencing for rapid detection of infection and antimicrobial resistance.Frontiers in microbiology · 2023Article
- Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sperm cell motility and morphology observed under the bright field microscopy are the only criteria for selecting a particular sperm cell during Intracytoplasmic Sperm Injection (ICSI) procedure of Assisted Reproductive Technology (ART). Several factors such as oxidative stress, cryopreservation, heat, smoking and alcohol consumption, are negatively associated with the quality of sperm cell and fertilization potential due to the changing of subcellular structures and functions which are overlooked. However, bright field imaging contrast is insufficient to distinguish tiniest morphological cell features that might influence the fertilizing ability of sperm cell. We developed a partially spatially coherent digital holographic microscope (PSC-DHM) for quantitative phase imaging (QPI) in order to distinguish normal sperm cells from sperm cells under different stress conditions such as cryopreservation, exposure to hydrogen peroxide and ethanol. Phase maps of total 10,163 sperm cells (2,400 control cells, 2,750 spermatozoa after cryopreservation, 2,515 and 2,498 cells under hydrogen peroxide and ethanol respectively) are reconstructed using the data acquired from the PSC-DHM system. Total of seven feedforward deep neural networks (DNN) are employed for the classification of the phase maps for normal and stress affected sperm cells. When validated against the test dataset, the DNN provided an average sensitivity, specificity and accuracy of 85.5%, 94.7% and 85.6%, respectively. The current QPI + DNN framework is applicable for further improving ICSI procedure and the diagnostic efficiency for the classification of semen quality in regard to their fertilization potential and other biomedical applications in general.
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