A Public Benchmark Study for Sperm DNA Fragmentation Prediction and Low-Risk Patients Identification

Objective: Sperm DNA fragmentation (SDF) tests such as Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and Sperm Chromatin Structure Assay, which is reported by indices such as the DNA fragmentation Index (DFI), are valuable tools in the assessment of male infertility. However, they are underutilized due to their limited accessibility and high cost. Identifying candidates who are unlikely to benefit from SDF testing could optimize clinical decision-making and reduce unnecessary expenses. This study aims to predict DFI and TUNEL values using routinely available semen parameters and age, which are easily accessible and cost-effective. Additionally, it seeks to develop a clinical decision support system (CDSS) to assist in guiding recommendations for SDF testing. Methods: Utilizing a newly proposed dataset of 10,000 infertile men, we develop a machine learning pipeline that includes pre-processing, feature engineering, and the development of models for predicting DFI and TUNEL values. This pipeline enables the establishment of a comprehensive benchmark and introduces a novel composite feature called the Unhealthy Sperm Score (USS), which serves as a risk indicator of sperm dysfunction. Additionally, a CDSS was constructed to identify patients with low DNA fragmentation risk (DFI < 30 and TUNEL < 20) based on their sperm profile and age. Results: The USS score demonstrated the highest correlation with both DFI and TUNEL, outperforming single semen parameters, and proved to be the primary driver for SDF predictions. Additionally, the developed CDSS achieved high precision in identifying low-risk individuals (94.83% for DFI and 96.15% for TUNEL). %We tested the tool on 1,000 cases and successfully bypassed 624 patients who may safely defer DNA fragmentation testing. Conclusion: Our work offers a benchmark dataset on predicting SDF tests (DFI and TUNEL), and a first-of-its-kind CDSS model for separating low-risk patients from high-risk ones, introducing new avenues for cost-effective, personalized male infertility care. The dataset and implementation code are publicly available at: https://github.com/HealMaDe/SDF_prediction