Infertility is a global concern, affecting approximately 72.4 million individuals worldwide. According to the World Health Organization (WHO), around 9% of couples face fertility issues, with male factors contributing to 50% of these cases.
The initial step in diagnosing male infertility typically involves conventional semen analysis. This test assesses spermatogenesis, the patency of seminal ducts, and glandular secretory activity, following the standards outlined in the WHO’s Laboratory Manual for the Examination and Processing of Human Semen.
In addition to semen analysis, serum hormone levels are evaluated to further investigate male infertility. Key hormones measured include LH, FSH, total testosterone, E2, PRL, and the testosterone-to-E2 ratio. These measurements provide insight into testicular function and the endocrine status of the hypothalamic-pituitary-testicular axis. Pulsatile GnRH secretion triggers the release of FSH and LH from the anterior pituitary, with FSH promoting spermatogenesis through Sertoli cells. Sertoli cells produce inhibin B, while Leydig cells secrete testosterone, which is converted to E2 by aromatase. Both inhibin B and E2 exert negative feedback on the hypothalamus and pituitary gland, regulating FSH and testosterone levels. Notably, elevated FSH often indicates spermatogenic dysfunction, although LH and testosterone levels may remain normal.
Previous studies have identified significant correlations between semen analysis results and serum hormone levels. Subsequent research has further explored associations between FSH, LH, and testosterone with semen analysis outcomes.
Traditional sperm analysis methods are labor-intensive and require manual inspection under a microscope. Furthermore, cultural stigma in some regions discourages men from undergoing these tests. While at-home sperm analysis kits offer a self-testing option, they cannot replace comprehensive laboratory evaluations. Consequently, there is a growing interest in finding alternative methods for assessing male infertility risk.
Machine learning, an advanced technology in artificial intelligence (AI), offers a promising solution. Unlike traditional statistics, which relies on hypothesis-driven data collection and analysis, machine learning enables computers to identify patterns and insights from large datasets without prior programming. This approach can process vast amounts of data and uncover correlations that may not be evident through conventional methods.
By applying machine learning to predict male infertility based solely on serum hormone levels, it may be possible to develop a screening system that eliminates the need for traditional semen analysis. If successful, such a system could revolutionize infertility diagnostics and make the process more accessible and less stigmatized.
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