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

• URL: http://arxiv.org/abs/2601.10070v1
• Date: Thu, 15 Jan 2026 04:51:36 GMT
• Title: Comparative Evaluation of Deep Learning-Based and WHO-Informed Approaches for Sperm Morphology Assessment
• Authors: Mohammad Abbadi,
• Abstract summary: Assessment of sperm morphological quality remains a critical yet subjective component of male fertility evaluation.<n>This study presents a comparative biomedical artificial intelligence framework evaluating an image-based deep learning model (HuSHeM)<n>HuSHeM model was trained on high-resolution sperm morphology images and evaluated using an independent clinical cohort.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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