Quantitative selection of single human sperm with high DNA integrity for intracytoplasmic sperm injection

A Stained-Free Sperm Morphology Measurement Method Based on Multi-Target Instance Parsing and Measurement Accuracy Enhancement

Sperm morphology assessment plays a vital role in semen analysis and the diagnosis of male infertility. By quantitatively analyzing the morphological characteristics of the sperm head, midpiece, and tail, it provides essential insights for assisted reproductive technologies (ARTs), such as in vitro fertilization (IVF). However, traditional manual evaluation methods not only rely on staining procedures that can damage the cells but also suffer from strong subjectivity and inconsistent results, underscoring the urgent need for an automated, accurate, and non-invasive method for multi-sperm morphology assessment. To address the limitations of existing techniques, this study proposes a novel method that combines a multi-scale part parsing network with a measurement accuracy enhancement strategy for non-stained sperm morphology analysis. First, a multi-scale part parsing network integrating semantic segmentation and instance segmentation is introduced to achieve instance-level parsing of sperm, enabling precise measurement of morphological parameters for each individual sperm instance. Second, to eliminate measurement errors caused by the reduced resolution of non-stained sperm images, a measurement accuracy enhancement method based on statistical analysis and signal processing is designed. This method employs an interquartile range (IQR) method to exclude outliers, Gaussian filtering to smooth data, and robust correction techniques to extract the maximum morphological features of sperm. Experimental results demonstrate that the proposed multi-scale part parsing network achieves 59.3% APvolp, surpassing the state-of-the-art AIParsing by 9.20%. Compared to evaluations based solely on segmentation results, the integration of the measurement accuracy enhancement strategy significantly reduces measurement errors, with the largest reduction in errors for head, midpiece, and tail measurements reaching up to 35.0%.

Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss

RESEARCH QUESTION

Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos?

DESIGN

In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A).

RESULTS

Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%.

CONCLUSIONS

This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.

Aberrant protamination in sperm correlates to anomalous nuclear and cytoplasmic architectures in infertile males with sperm dysmorphology

Aberrant sperm protamination is linked to sperm dysmorphology and nuclear chromatin condensation. Yet, its effects on sperm cytoplasmic maturation remain largely unexplored. The relationships of protamines, sperm morphology, DNA damage, and cytoplasmic remodeling were illustrated in this study to provide fresh perspectives on the mechanisms of male infertility. A total of 205 infertile males were allocated into 5 groups according to the percentage of spermatozoa exhibiting abnormal morphology within their samples. Sperm concentration, motility, abnormal sperm morphology, cytoplasmic droplets (CDs), and excess residual cytoplasm (ERC) were analyzed according to the World Health Organization manual (2010). Sperm nuclear vacuoles (NVs) were determined by propidium iodide (PI) staining. Sperm protamine expressions (P1 and P2) were detected by western blot. DNA damage was measured by acridine orange test (AOT) to calculate the proportion of sperm with single-strand DNA breaks (SSBs). Our data showed that sperm concentration and motility in infertile males significantly decreased with the severity of abnormal sperm morphology (both P < 0.01). P1 level, P1/P2 ratio, and SSB rate increased with the severity of sperm dysmorphology, whilst the P2 level decreased (all P < 0.01). NVs, CDs, and ERC were more common in males with sperm dysmorphology and positively correlated with the SSB rate (all P < 0.01). The relationships between the SSB rate and the P1/P2 ratio were also significant ( P < 0.01). Aberrant protamination may cause sperm dysmorphology and compromise male fertility by impairing sperm's nucleus and cytoplasm maturation, with the P1/P2 ratio potentially serving as a valuable indicator of sperm quality and male fertility.

Sperm DNA integrity does play a crucial role for embryo development after ICSI, notably when good-quality oocytes from young donors are used

Based on the inconsistent literature published thus far involving infertile patients, whether intracytoplasmic sperm injection (ICSI) allows overcoming total fertilization failure due to sperm DNA fragmentation is still unclear. Related to this, female factors, which may have a significant impact on assisted reproduction outcomes, can mask male infertility. In this scenario, evaluating ICSI outcomes following cycles using healthy donor gametes could shed light on this realm, as it would avoid the influence of (un)known confounding factors present in infertile individuals. The present work, therefore, aimed to address whether single- and double-stranded sperm DNA fragmentation leads to impaired ICSI outcomes in double gamete donation cycles. The study also compared these double-gamete donation cycles to cycles in which only sperm were donated and oocytes were obtained from infertile patients. Two cohorts were included: (a) the Donor-Donor (DD) cohort, which included 27 semen donor samples used in 49 ICSI cycles with young healthy oocyte donors; and (b) the Donor-Infertile (DI) cohort, which involved 34 semen donor samples used in 57 ICSI cycles with oocytes from patients. Single- and double-stranded sperm DNA breaks were determined with alkaline and neutral Comet assays, respectively; ICSI was conducted following standard protocols and embryos were monitored through time-lapse microscopy. In the DD cohort, the percentage of sperm with high overall DNA damage correlated with fertilization rates (Rs = - 0.666; P < 0.001) and with the percentage of blastocysts per injected oocyte (Rs = - 0.414; P = 0.040). In addition, sperm DNA damage delayed the first embryo division (Rs = 0.421; P = 0.036), and development from the 8-cell to the morula stage (Rs = 0.424; P = 0.034). In contrast, double-stranded DNA breaks had no effect in this cohort. As far as the DI cohort is concerned, while overall sperm DNA damage was not found to be correlated to fertilization or blastocyst rates, pronuclei formation following ICSI was delayed when the incidence of double-stranded DNA breaks was high (Rs = 0.485; P = 0.005). In conclusion, this study, which is the first involving double donation cycles (i.e., a donor-donor cohort), supports that sperm DNA damage has a detrimental impact on fertilization rates after ICSI, and delays embryo development. Moreover, the use of oocytes from infertile individuals is suggested to hide the male-factor effect.

Lab on a chip devices for fertility: from proof-of-concept to clinical impact

Microfluidics offers tremendous opportunities to understand the underlying biology of fertilization at the single-cell level and improve infertility management, however, its true clinical impact is yet to be realized. Lab-on-a-chip devices have generally failed to diffuse into clinical practice due to issues associated with their translation or their practicality and performance in clinical settings. In this perspective, I reflect on how the full potential of microfluidic technologies for fertility can be realized by considering regulatory and manufacturing considerations at the development stage and by redefining our developmental goals to directly target the ultimate clinical needs. I also challenge the common rationale around developing technologies for infertility treatment based on reducing cost and complexity in operation as the ultimate outcome is invaluable, human life.