Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index

Artificial Intelligence in Human Reproduction

The use of artificial intelligence (AI) in human reproduction is a rapidly evolving field with both exciting possibilities and ethical considerations. This technology has the potential to improve success rates and reduce the emotional and financial burden of infertility. However, it also raises ethical and privacy concerns. This paper presents an overview of the current and potential applications of AI in human reproduction. It explores the use of AI in various aspects of reproductive medicine, including fertility tracking, assisted reproductive technologies, management of pregnancy complications, and laboratory automation. In addition, we discuss the need for robust ethical frameworks and regulations to ensure the responsible and equitable use of AI in reproductive medicine.

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.

Biomarker-based human and animal sperm phenotyping: the good, the bad and the ugly†

Conventional, brightfield-microscopic semen analysis provides important baseline information about sperm quality of an individual; however, it falls short of identifying subtle subcellular and molecular defects in cohorts of "bad," defective human and animal spermatozoa with seemingly normal phenotypes. To bridge this gap, it is desirable to increase the precision of andrological evaluation in humans and livestock animals by pursuing advanced biomarker-based imaging methods. This review, spiced up with occasional classic movie references but seriously scholastic at the same time, focuses mainly on the biomarkers of altered male germ cell proteostasis resulting in post-testicular carryovers of proteins associated with ubiquitin-proteasome system. Also addressed are sperm redox homeostasis, epididymal sperm maturation, sperm-seminal plasma interactions, and sperm surface glycosylation. Zinc ion homeostasis-associated biomarkers and sperm-borne components, including the elements of neurodegenerative pathways such as Huntington and Alzheimer disease, are discussed. Such spectrum of biomarkers, imaged by highly specific vital fluorescent molecular probes, lectins, and antibodies, reveals both obvious and subtle defects of sperm chromatin, deoxyribonucleic acid, and accessory structures of the sperm head and tail. Introduction of next-generation image-based flow cytometry into research and clinical andrology will soon enable the incorporation of machine and deep learning algorithms with the end point of developing simple, label-free methods for clinical diagnostics and high-throughput phenotyping of spermatozoa in humans and economically important livestock animals.

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.