A Novel Enhanced t-SNE Framework for Male Fertility Prediction with Multi-Factorial Data

Abstract

The prevalence of male infertility is very high, and it is usually underdiagnosed, with Sperm DNA Fragmentation (SDF) affecting an estimated 30% of infertile men, leading to IVF failure, miscarriage, and poor embryo quality.  Recent therapeutic methods are showing less active repair of DNA damage, ranging from antioxidant supplements to sperm selection techniques, which leads to a limited success rate. To address these issues, this study presents an AI-based GenoRepair, an initial-stage, real-time DNA repair system that utilizes AI and nanotechnology. The CRISPR-Cas9-loaded magnetic lipid nanoparticles (LNPs) target and repair fragmented DNA through an integrated system of a hybrid Convolutional Neural Network (CNN) and a Transformer model for rapid SDF detection. Through individual sperm epigenetics and motility profiles, repair protocols are customized using reinforcement learning, with a focus on demonstrating DNA integrity and viability post-repair through in vitro validation. The results demonstrate a 65% increase in IVF success rates using the minimally invasive and effective method. At the molecular level, GenoRepair AI represents a paradigm shift in fertility medicine, offering a precision-engineered and scalable solution. This technology enhances reproductive outcomes, which also aids in future interventions, and AI is guided by regenerative medicine.