Comparative Evaluation of Deep Learning-Based and WHO-Informed Approaches for Sperm Morphology Assessment

TL;DR

This study compares a deep learning model and WHO-based criteria for sperm morphology assessment, showing that the AI model offers superior predictive accuracy and clinical utility, potentially improving male fertility evaluations.

Contribution

It introduces a deep learning framework for sperm morphology assessment that outperforms traditional WHO-based criteria in predictive accuracy and clinical utility.

Findings

HuSHeM model shows higher AUC than WHO(+SIRI)

Deep learning improves prediction under class imbalance

Model demonstrates closer calibration and greater clinical benefit

Abstract

Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation, often limited by inter-observer variability and resource constraints. This study presents a comparative biomedical artificial intelligence framework evaluating an image-based deep learning model (HuSHeM) alongside a clinically grounded baseline derived from World Health Organization criteria augmented with the Systemic Inflammation Response Index (WHO(+SIRI)). The HuSHeM model was trained on high-resolution sperm morphology images and evaluated using an independent clinical cohort. Model performance was assessed using discrimination, calibration, and clinical utility analyses. The HuSHeM model demonstrated higher discriminative performance, as reflected by an increased area under the receiver operating characteristic curve with relatively narrow confidence intervals compared to WHO(+SIRI). Precision-recall analysis further indicated improved performance under class imbalance, with higher precision-recall area values across evaluated thresholds. Calibration analysis indicated closer agreement between predicted probabilities and observed outcomes for HuSHeM, while decision curve analysis suggested greater net clinical benefit across clinically relevant threshold probabilities. These findings suggest that image-based deep learning may offer improved predictive reliability and clinical utility compared with traditional rule-based and inflammation-augmented criteria. The proposed framework supports objective and reproducible assessment of sperm morphology and may serve as a decision-support tool within fertility screening and referral workflows. The proposed models are intended as decision-support or referral tools and are not designed to replace clinical judgment or laboratory assessment.