Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm.

Area of Science:

• Biomedical Engineering
• Reproductive Biology
• Artificial Intelligence in Medicine

Background:

• Acrosome reaction (AR) analysis in sperm is crucial for fertility assessments.
• Subjectivity in expert analysis of AR can impact diagnostic accuracy and consistency.

Purpose of the Study:

• To develop an automated, objective, and consistent system for sperm AR analysis using artificial intelligence.
• To enhance the accuracy and efficiency of sperm diagnosis in clinical settings.

Main Methods:

• Convolutional Neural Networks (CNNs), specifically ResNet 50 and Inception-ResNet v2 architectures, were employed.
• Models were trained on microscopic sperm images at 400× and 1000× magnification.
• The system was validated against expert assessments for AR ratio calculation.

Main Results:

• The AI system accurately recognized subtle changes in sperm membranes.
• The Inception-ResNet v2-based system achieved a mean average precision exceeding 97%.
• The system's AR ratio calculations closely matched expert results, with significantly reduced analysis time.

Conclusions:

• The developed AI system (ARCS) provides consistent AR sperm detection, minimizing human error.
• This AI assistance system demonstrates feasibility and benefits for routine sperm diagnosis.
• The study highlights the potential of AI in streamlining and improving the accuracy of fertility diagnostics.