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.
Men between the ages of 18 and 50 who come to the clinic to undergo an ICSI cycle.
Men between the ages of 18 and 50 who come to the clinic to undergo an artificial insemination cycle.
All embryos will be placed in a time-lapse incubator.
All women over 18 years of age who have obtained a MII number greater than or equal to 2 in oocyte retrieval, without excluding couples from the oocyte donation programme.
All women diagnosed with recurrent pregnancy loss.
All semen samples obtained by testicular biopsy.
All donor semen samples.
The study population will include patients who attend the IVIRMA Valencia clinic for a cycle of artificial insemination or a cycle of ICSI and undergo sperm selection using the Harvester microfluidic technique.
Cohort
Prospective
Sperm sellection
Diagnostic testSmall aliquots of 100 fresh human semen samples will be analysed using the SwimCount™ Harvester system, a CE-marked microfluidic technology patented by MCount and designed for routine clinical use.
DIAGNOSIS TOOL
Diagnostic testThe diagnostic tool developed will be validated by analyzing small aliquots of 100 additional fresh semen samples processed with the integrated microfluidic system (SwimCount™ Harvester) and the CASA system with artificial intelligence. To evaluate the diagnostic efficiency of the tool, the results obtained from the semen quality analysis will be compared with those obtained from the analysis of these samples when processed using the reference technique, based on conventional methods employed by fertility specialists. Finally, to determine the effectiveness of the developed tool, the clinical results obtained from assisted reproduction treatment will be compared with the diagnosis predicted by the tool, with the aim of evaluating its predictive capacity. Translated with DeepL.com (free version)
Development of a Male Infertility Diagnosis Tool
Tool development
Prediction Capability Identification
Measure of correlation between clinical data and tool prediction
Development of an AI algorithm
Development of an AI algorithm
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