Automatic identification of human spermatozoa with zona pellucida-binding capability using deep learning.

Area of Science:

• Reproductive Medicine
• Artificial Intelligence in Healthcare
• Sperm Morphology Analysis

Background:

• Traditional semen analysis, including World Health Organization (WHO) sperm morphology grading, has limited predictive power for fertilization outcomes in assisted reproductive technology (ART).
• Manual sperm assessment is subjective, labor-intensive, and prone to high inter-/intra-assay variations, necessitating more objective and accurate methods.
• Deep learning offers automated image analysis potential, but prior algorithms using WHO criteria have shown limited success in predicting ART outcomes.

Purpose of the Study:

• To develop and validate a deep-learning algorithm capable of identifying human spermatozoa with zona pellucida (ZP)-binding capability.
• To determine if this algorithm can predict fertilization potential in assisted reproductive technology (ART) independently of conventional semen analysis.
• To establish a novel, objective standard for sperm morphology evaluation based on fertilizing ability for clinical application.

Main Methods:

• A VGG13 deep-learning model was fine-tuned using 1083 Diff-Quik stained sperm images (ZP-bound vs. ZP-unbound) to classify spermatozoa based on morphological features.
• The model's performance was assessed using confusion matrices (accuracy, specificity, sensitivity, precision) and receiver-operating characteristic (ROC) curves (AUC).
• Clinical validation involved analyzing sperm images from 117 men undergoing IVF, correlating model predictions with fertilization rates and comparing performance against conventional semen analysis.

Main Results:

• The fine-tuned VGG13 model achieved high classification performance: 96.7% accuracy, 97.6% sensitivity, 96.0% specificity, and 95.2% precision in distinguishing ZP-bound spermatozoa.
• The model demonstrated excellent generalization ability, with a strong correlation between predicted ZP-binding percentages and actual IVF fertilization rates across clinical groups.
• A clinical threshold of 4.9% ZP-binding ability was established, and the model outperformed conventional semen analysis in identifying patients at risk of IVF fertilization failure.

Conclusions:

• A novel deep-learning model can accurately identify human spermatozoa with ZP-binding capability, offering a more reliable predictor of fertilization potential than conventional semen analysis.
• This AI-driven approach can identify couples at high risk of unexpected IVF fertilization failure, enabling timely intervention with alternative insemination methods.
• Further validation is needed across diverse image qualities and larger sample sizes to broaden the clinical applicability of this advanced diagnostic tool.