Clinical Research Directory

Functional Development and Clinical Validation of a Diagnostic Tool Based on Artificial Intelligence for the Assessment of Sperm Quality and the Selection of the Optimal In Vitro Fertilisation (IVF) Treatment

Infertility is a growing global health problem affecting millions of couples worldwide, with male infertility accounting for approximately half of all cases. In the physiological environment, sperm go through an exhaustive selection process in the female reproductive tract before reaching the oocyte. During this journey, progressive mobility and morphology are key parameters for achieving fertilisation. Therefore, before starting an assisted reproduction treatment, it is essential to analyse and process the semen sample to assess the fertile potential, select the most optimal sperm and determine the most appropriate treatment. Conventional methods of semen processing, such as density gradient centrifugation (DGC) and Swim-up washing of motile sperm, have significant limitations. These include interobserver and interlaboratory subjectivity, as well as damage to sperm DNA caused by centrifugation. Alternatively, microfluidics, which simulates natural selection, allows higher counts of morphologically normal, progressive motile sperm to be obtained. On the other hand, the CASA (computer-assisted sperm analysis) system has improved the standardisation and quality of semen analysis. Furthermore, the incorporation of Artificial Intelligence (AI) into semen quality analysis represents a promising opportunity, as it improves efficiency, accuracy and standardisation, and has the potential to increase success rates in assisted reproduction treatments. This project aims to develop an innovative AI-based diagnostic tool to address male infertility. The tool will integrate microfluidic technology and the CASA system to analyse semen quality, calculate fertilisation potential and recommend personalised treatments with an estimate of success. Trained with large volumes of biological and clinical data, it will provide a comprehensive and patient-specific diagnosis by identifying complex relationships between multiple variables. Finally, a comparative study will be conducted to evaluate laboratory indicators and clinical outcomes of cycles using this tool versus those using conventional methods.

Use of the SiDTM v2.0 Algorithm to Assist Embiyologists in Sperm Selection During ICSI Procedures

Infertility is defined as a failure of a couple to achieve a pregnancy after 12 or more months of unprotected intercourse. Males are found to be solely responsible for 20-30% of infertility cases but contribute to 50% of cases overall. The selection of sperm to microinject is completely subjective and there is high intra- and inter- observer variability. SiDTM v2.0 is an algorithm which analyses real-time seminal samples located at ICSI dishes. Particularly, it assesses morphology and several motility parameters of each sperm, and it assigns a categorical and numerical score to each one. Categorical scores are represented by colours: green colour for optimal sperm, yellow for good sperm, orange for medium-quality sperm and red for low-quality sperm. Numerical scores ranged from 0 to 100, with higher scores for those best-quality sperm. SiDTM v2.0 can reduce subjectivity of the sperm selection process to the maximum, selecting the optimal sperm in real time. In addition, it could help junior embryologists to perform this complex and tedious procedure, which is the sperm selection for ICSI. To carry out the study, we will conduct a prospective cohort study in a total of 100 couples. Therefore, the aim of this study is to validate SiDTM v2.0 as an useful Artificial Intelligence-tool for sperm selection; that means achieving , at least, same clinical results as sperm selection performed by the embryologist.

The Effects of Diet on Small Non-coding RNA I Sperm Cells and the Possible Effects on Reproductive Success: Diet Intervention Effects on Sperm

In this study, patients with infertility treated with IVF at the Center of Reproductive Medicine in Linköping, Sweden, will receive recipes and food according to Nordic dietary guidelines. The purpose is to investigate whether a short diet intervention is sufficient to improve sperm quality and shift the small non-coding RNA profile. In our previous study sperm showed rapid response to diet.

The Impact of Lifestyle Intervention on Weight and Fertility in Obese Males

Obesity, defined by WHO standards as having a body mass index (BMI) equal to or greater than 30 kg/m², affects approximately 800 million people worldwide. It is evident that obesity has become a serious public health issue, resulting in significant health burdens. Previous systematic reviews have indicated an association between obesity and male factor infertility. In populations undergoing assisted reproductive technology (ART), some studies have shown a correlation between increased male BMI and adverse ART outcomes. Furthermore, the negative effects of obesity may also be transmitted to offspring through genetic and epigenetic changes in reproductive cell DNA, increasing their risk of obesity, metabolic diseases, or other chronic conditions. Currently, there is a lack of data on the impact of weight loss in obese men on fertility, and it is unclear which nutritional pattern in lifestyle interventions can more effectively control weight, improve semen quality, and address related endocrine issues in obese men, thereby improving reproductive treatment outcomes. Based on previous literature, we hypothesize that lifestyle interventions, particularly strict low-carbohydrate diets combined with lifestyle guidance, may offer greater health benefits for obese men. These benefits include effective weight loss, improvement in semen parameters, reproductive metabolic health, quality of life related to reproductive health, and the impact on reproductive treatment outcomes. This provides a basis for non-pharmacological intervention strategies and methods for the health of obese men.