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

NCT07185984 | Not Yet Recruiting

• 1 other identifier: 2407-VLC-156-MM
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Infertility (IVF Patients); Male Fertility; Sperm Selection

Keywords

Male infertility; Microfluidics; Artificial intelligence; Sperm quality

Outcome Measures

Primary Outcomes (1)

• Development of a Male Infertility Diagnosis Tool

  Tool development

  2 YEARS

Secondary Outcomes (2)

• Prediction Capability Identification

  2 Years

• Development of an AI algorithm

  2 years

Study Arms (2)

PRELIMINARY SPERM ANALISYS

Small aliquots of 100 fresh human semen samples will be analysed using the SwimCount™ Harvester system. The sperm variables analyzed will include concentration, motility (total and progressive), morphology, movement speed, and trajectory.

Diagnostic Test: Sperm sellection

DIAGNOSIS TOOL VALIDATION

100 additional fresh semen samples processed with the integrated microfluidics system (SwimCount™ Harvester) and the CASA system with artificial intelligence

Diagnostic Test: DIAGNOSIS TOOL

Interventions

Sperm sellection DIAGNOSTIC_TEST

Small 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.

PRELIMINARY SPERM ANALISYS

DIAGNOSIS TOOL DIAGNOSTIC_TEST

The 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)

DIAGNOSIS TOOL VALIDATION

Eligibility Criteria

• Age: 18 Years - 50 Years
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Probability Sample

Study Population

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.

You may qualify if:

• 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 men and women with a previously known normal karyotype.
• Informed consent (IC) provided and signed by patients.

You may not qualify if:

• All women diagnosed with recurrent pregnancy loss.
• All semen samples obtained by testicular biopsy.
• All donor semen samples.

Sponsors & Collaborators

• Instituto Valenciano de Infertilidad, IVI VALENCIA: lead

Study Sites (1)

IVI Valencia

Valencia, 46015, Spain

Location

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MeSH Terms

Conditions

Infertility; Infertility, Male

Condition Hierarchy (Ancestors)

Genital Diseases; Urogenital Diseases; Genital Diseases, Male; Male Urogenital Diseases

Study Officials

• Marcos Meseguer, PhD

  IVIRMA Valencia

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Marcos Meseguer, PhD

CONTACT

+34 963050999; marcos.meseguer@ivirma.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 15, 2025
• First Posted: September 22, 2025
• Study Start: November 1, 2025
• Primary Completion (Estimated): November 1, 2027
• Study Completion (Estimated): November 1, 2027
• Last Updated: September 22, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)