Novel sperm chromatin dispersion test with artificial intelligence-aided halo evaluation: A comparison study with existing modalities

Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey

PURPOSE OF REVIEW

This review highlights the timely relevance of artificial intelligence in enhancing assisted reproductive technologies (ARTs), particularly in-vitro fertilization (IVF). It underscores artificial intelligence's potential in revolutionizing patient outcomes and operational efficiency by addressing challenges in fertility diagnoses and procedures.

RECENT FINDINGS

Recent advancements in artificial intelligence, including machine learning and predictive modeling, are making significant strides in optimizing IVF processes such as medication dosing, scheduling, and embryological assessments. Innovations include artificial intelligence augmented diagnostic testing, predictive modeling for treatment outcomes, scheduling optimization, dosing and protocol selection, follicular and hormone monitoring, trigger timing, and improved embryo selection. These developments promise to refine treatment approaches, enhance patient engagement, and increase the accuracy and scalability of fertility treatments.

SUMMARY

The integration of artificial intelligence into reproductive medicine offers profound implications for clinical practice and research. By facilitating personalized treatment plans, standardizing procedures, and improving the efficiency of fertility clinics, artificial intelligence technologies pave the way for value-based, accessible, and efficient fertility services. Despite the promise, the full potential of artificial intelligence in ART will require ongoing validation and ethical considerations to ensure equitable and effective implementation.

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis

Background and Objectives: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of semen analysis results remains quite poor despite years of practice and advancements. Artificial intelligence (AI) algorithms, which can analyze and synthesize large amounts of data, can address the unique challenges involved in semen analysis due to the high objectivity of current methodologies. This review addresses recent AI advancements in semen analysis. Materials and Methods: A systematic literature search was performed in the PubMed database. Non-English articles and studies not related to humans were excluded. We extracted data related to AI algorithms or models used to evaluate semen parameters from the original studies, excluding abstracts, case reports, and meeting reports. Results: Of the 306 articles identified, 225 articles were rejected in the preliminary screening. The evaluation of the full texts of the remaining 81 publications resulted in the exclusion of another 48 articles, with a final inclusion of 33 original articles in this review. Conclusions: AI and machine learning are becoming increasingly popular in biomedical applications. The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms. Furthermore, when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.

Gaussian clustering and quantification of the sperm chromatin dispersion test using convolutional neural networks

Sperm DNA fragmentation is a sign of sperm nuclear damage. The sperm chromatin dispersion (SCD) test is a reliable and economical method for the evaluation of DNA fragmentation. However, the cut-off value for differentiation of DNA fragmented sperms is fixed at 1/3 with limited statistical justification, making the SCD test a semi-quantitative method that gives user-dependent results. We construct a collection of deep neural networks to automate the evaluation of bright-field images for SCD tests. The model can detect valid sperm nuclei and their locations from the input images captured with a 20× objective and predict the geometric parameters of the halo ring. We construct an annotated dataset consisting of N = 3120 images. The ResNet 18 based network reaches an average precision (AP50) of 91.3%, a true positive rate of 96.67%, and a true negative rate of 96.72%. The distribution of relative halo radii is fit to the multi-peak Gaussian function (p > 0.99). DNA fragmentation is regarded as those with a relative halo radius 1.6 standard deviations smaller than the mean of a normal cluster. In conclusion, we have established a deep neural network based model for the automation and quantification of the SCD test that is ready for clinical application. The DNA fragmentation index is determined using Gaussian clustering, reflecting the natural distribution of halo geometry and is more tolerable to disturbances and sample conditions, which we believe will greatly improve the clinical significance of the SCD test.

Sperm Quality Assessment in Stallions: How to Choose Relevant Assays to Answer Clinical Questions

Stallion sperm analysis is indicated for infertility diagnosis, pre-sale expertise, production of fresh or frozen doses, and frozen straw quality control. Various collection methods are described, and numerous assays can be performed on semen. Determining an approach for each of these cases is challenging. This review aims to discuss how to obtain relevant clinical results, answering stallion owners' concerns. Semen can be collected with an artificial vagina on a phantom or a mare, by electro-ejaculation under anesthesia, or after pharmacological induction. The collection method influences the semen volume and concentration, while the total sperm number depends on the testicular production and collection frequency. In the seminal plasma, acidity, pro-oxidant activity, and some enzymes have repercussions for the semen quality and its conservation. Moreover, non-sperm cells of seminal plasma may impact semen conservation. Motility analysis remains a core parameter, as it is associated with fresh or frozen dose fertility. Computer-assisted motility analyzers have improved repeatability, but the reproducibility between laboratories depends on the settings that are used. Morphology analysis showing spermatozoa defects is useful to understand production and maturation abnormalities. Staining of the spermatozoa is used to evaluate viability, but recent advances in flow cytometry and in fluorochromes enable an evaluation of multiple intracellular parameters. Spermatozoa protein expression already has clinical applications, for example, as a fertility and freezing ability predictor. At present, stallion semen analysis ranges from macroscopic evaluation to assessing spermatozoa proteins. However, clinically, all these data may not be relevant, and the lack of standardization may complicate their interpretation.

Sperm DNA fragmentation testing in clinical management of reproductive medicine

Background

Approximately 8%-12% of couples worldwide face infertility, with infertility of individuals assigned male at birth (AMAB) contributing to at least 50% of cases. Conventional semen analysis commonly used to detect sperm abnormalities is insufficient, as 30% of AMAB patients experiencing infertility show normal results in this test. From a genetic perspective, the assessment of sperm DNA fragmentation (SDF) is important as a parameter of sperm quality.

Methods

In this narrative study, we review and discuss pathophysiological causes, DNA repair mechanisms, and management of high SDF. We then summarize literature exploring the association between SDF and reproductive outcomes.

Main Findings

Recent systematic reviews and meta-analyses have revealed a significant association between high SDF in AMAB individuals and adverse reproductive outcomes including embryo development, natural conception, intrauterine insemination, and in vitro fertilization. However, the association with live birth rates and pregnancy rates following intracytoplasmic injection remains inconclusive. The disparities among quantitative assays, inconsistent reference range values, absent high-quality prospective clinical trials, and clinical heterogeneity in AMAB patients with elevated SDF represent the main limitations affecting SDF testing.

Conclusion

The evaluation and management of SDF plays an important role in a subset of AMAB infertility, but widespread integration into clinical guidelines will require future high-quality clinical trials and assay standardization.