Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods

Area of Science:

• Reproductive Biology
• Artificial Intelligence
• Bioinformatics

Background:

• Declining male fertility rates necessitate advanced diagnostic tools.
• Environmental and lifestyle factors significantly impact semen quality.
• Existing methods for assessing male fertility potential can be enhanced with predictive analytics.

Purpose of the Study:

• To develop an artificial intelligence (AI) model for predicting semen characteristics.
• To assess the influence of environmental factors, lifestyle, and health on male fertility.
• To create a decision support system for evaluating male fertility potential.

Main Methods:

• Utilized a multilayer perceptron network, a type of artificial neural network (ANN).
• Collected semen samples from 123 healthy male volunteers for analysis.
• Administered validated questionnaires on lifestyle and health status to participants.

Main Results:

• The ANN model successfully predicted semen analysis results based on questionnaire data.
• Sperm concentration was the most accurately predicted parameter.
• Motility prediction showed significant accuracy, though slightly lower than concentration.

Conclusions:

• Developed an AI-driven methodology for predicting key semen parameters.
• This AI approach offers a valuable tool for early detection of male reproductive issues.
• The system can assist in selecting suitable candidates for sperm donation programs.