A new technique for sperm analysis is presented, measuring DNA fragmentation, morphology with virtual staining, and motility, all three criteria on the same individual unstained live cell. The method relies on quantitative stain‐free interferometric imaging, providing unique topographic structural and content maps of the cell, becoming available for the first time for clinical use, together with deep‐learning frameworks and least‐squares linear approximation. In the common clinical practice, only motility evaluation can be carried out on live human cells, while full morphological evaluation and DNA fragmentation assays require different staining protocols, and therefore cannot be performed on the same cell, resulting in inconsistencies in fertility evaluation. A clinic‐ready interferometric module is used to acquire dynamic sperm cells without chemical staining, together with deep learning to evaluate all three scores per cell with accuracy of 93.1%, 88%, and 90% for morphology, motility, and DNA fragmentation, respectively. It is shown that the expected number of cells that pass all three criteria based on the current evaluations performed separately does not correspond with the number of cells that pass all criteria, demonstrating the importance of the suggested method. The proposed stain‐free evaluation method is expected to decrease uncertainty in infertility diagnosis, increasing treatment success rates.
We present a new technique for simultaneously analyzing morphology, motility and DNA fragmentation of live human sperm cells at the single-cell level for male fertility evaluation. It relies on quantitative stain-free interferometric imaging and multiple deep-learning frameworks. In the common clinical practice, only motility evaluation is carried out on live human cells, while full morphological evaluation and DNA fragmentation assays require different staining protocols, and therefore cannot be performed simultaneously on the same cell. This results in a lack of information regarding the intersection of these scores. We use a clinic-ready interferometric module and deep learning to acquire dynamic sperm cells without chemical staining, and evaluate all three scores per each cell together with virtual staining. We show that the number of cells that pass each criterion separately does not accurately predict how many would pass all criteria, thus the triple evaluation per cell is necessary for accurate fertility grading. This stain-free evaluation is expected to decrease the uncertainty in male fertility evaluation, as well as be applied for sperm selection during in vitro fertilization.
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