Development and validation of a machine learning model for sperm DNA fragmentation rate in infertile men: a multicenter retrospective study - Scorecard - MDSpire
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Development and validation of a machine learning model for sperm DNA fragmentation rate in infertile men: a multicenter retrospective study
Clinical Scorecard: Creation and assessment of a machine learning-based model to predict sperm DNA fragmentation rates in men with infertility: a retrospective multicenter analysis
At a Glance
Category
Detail
Condition
Sperm DNA fragmentation index (DFI) in male infertility
Key Mechanisms
Influenced by clinical and semen parameters, lifestyle factors, and oxidative stress damage
Target Population
Infertile men
Care Setting
Reproductive medicine centers
Key Highlights
Developed a machine learning model to predict high sperm DFI using clinical and semen parameters
Random Forest model showed the best performance with AUC of 0.979 in the development cohort
Core factors included sperm motility, concentration, viability, and lifestyle factors like smoking and stress
Model exhibits miscalibration in external validation, indicating overestimation of risk
A publicly accessible online DFI prediction platform was created
Guideline-Based Recommendations
Diagnosis
Incorporate sperm DNA fragmentation index (DFI) into sperm quality testing as recommended by WHO
Management
Utilize machine learning models to assess sperm DFI for clinical decision-making
Monitoring & Follow-up
Prospective validation and recalibration of the model in independent populations are required
Risks
Model shows systematic overestimation of risk and insufficient dispersion of predictions
Patient & Prescribing Data
Infertile male patients from two reproductive medicine centers in Shanghai, China
