Infertility is a problem estimated to affect 72.4 million females and males worldwide. The WHO estimates that 9% of couples worldwide struggle with fertility problems and that male factors are involved in 50% of them.
Conventional semen analysis is the first step in the diagnosis of male infertility. The results of semen analysis reflect spermatogenesis in the testes, the patency of seminal ducts, and the glandular secretory activity3. The evaluation of semen parameters is currently based on the standards defined in the Laboratory Manual for the Examination and Processing of Human Semen created by WHO. Conventional sperm analysis methods involve complex, manual inspection with a microscope, they are labor intensive. In addition, many men are unwilling to be tested due to social stigma in certain parts of the world. Although there are at-home sperm analysis kits for people to examine sperm condition by themselves, they are not a substitute for laboratory analysis. Therefore, a new technology called machine learning is now a possible ancillary tool to predicting infertility. Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. Statistics and machine learning are quite different. Statistics was born out of statistical research, while machine learning was born out of computer science. They are very different in terms of data acquisition. Statistics is based on processing the data collected for a purpose. Machine learning does not necessarily involve data obtained with a purpose from the beginning. In other words, statistics focuses on collecting and analyzing data to confirm hypotheses and estimates, whereas machine learning starts from the data, searches for regularities and features in it, and applies them as
follicle-stimulating hormone (FSH), luteinizing hormone (LH), and testosterone.
- Machine learning algorithms analyze patterns in hormone levels and correlate them with known fertility outcomes. The AI system achieves 100% accuracy in identifying non-obstructive azoospermia, a condition where sperm is absent in the semen due to issues in sperm production.
- By providing an accessible and efficient alternative to AI, the advantage of machine learning is that it can deal with the huge amounts of big data.
Semen analysis is important for evaluating male infertility, and often the first test ordered when a couple presents for a fertility check or when a man is interested in permanent contraception. Sperm motility, morphology, velocity, and concentration are investigated using microscopes and counting chambers by skilled embryologists. Other methods, such as CASA (computer-assisted semen analysis), which uses algorithms to automatically track spermatozoa, are also effective and are able to present qualitative information on sperm motility. However, semen analysis can only be done at a fertility clinic in most cases. In addition, many men feel uncomfortable about having semen analysis.
The AI tool evaluates various hormones in the blood, including semen analysis, this blood test can help detect potential fertility issues early, allowing men to seek appropriate interventions or treatments sooner. In conclusion, the development of an AI-powered blood test for male infertility is a significant advancement in reproductive health. By leveraging hormone analysis and machine learning, this tool offers a more accessible and efficient means of identifying men at risk of infertility.
