ArticleProceedings of the National Academy of Sciences of the United States of America2020
Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- Label-Free Detection of Acrosome Reaction of Human Sperm Based on Diffraction Phase Microscopy.Chemical & biomedical imaging · 2026Article
- From genotype to phenotype: decoding mutations in blasts by holo-tomographic flow cytometry.Light, science & applications · 2025Article
- Modeling diffusive search by non-adaptive sperm: Empirical and computational insights.PLoS computational biology · 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
- Artificial Intelligence in Andrology: A New Frontier in Male Infertility Diagnosis and Treatment.Current urology reports · 2025Review
- A Narrative Review on the Sperm Selection Methods in Assisted Reproductive Technology: Out with the New, the Old Is Better?Journal of clinical medicine · 2025Review
- The Andrological Landscape in the Twenty-First Century: Making Sense of the Advances in Male Infertility Management for the Busy Clinicians.International journal of environmental research and public health · 2024Review
- Label-free, high-throughput holographic imaging to evaluate mammalian gametes and embryos†.Biology of reproduction · 2024Review
- Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part I.Journal of biomedical optics · 2024Review
- Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics.The world journal of men's health · 2024Review
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023Article
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Unique Deep Radiomic Signature Shows NMN Treatment Reverses Morphology of Oocytes from Aged Mice.Biomedicines · 2022Article
- Live-dead assay on unlabeled cells using phase imaging with computational specificity.Nature communications · 2022Article
- Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning.Nature cell biology · 2021Article
- Article
- Large-scale phase retrieval.Light, science & applications · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Does artificial intelligence have a role in the IVF clinic?Reproduction & fertility · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.
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Registered trials
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