🤖 AI Summary
Male factors account for approximately half of global infertility cases, and sperm DNA fragmentation (SDF) is a critical biomarker influencing in vitro fertilization (IVF) success rates. Conventional chemical assays for SDF quantification—though accurate—are destructive, rendering sperm nonviable for subsequent assisted reproductive procedures. To address this limitation, we propose the first noninvasive, stain-free SDF prediction framework based solely on unstained bright-field microscopic images, preserving full sperm viability. Our method integrates a multi-scale deep convolutional neural network, transfer learning, and robust data augmentation strategies. Evaluated on clinical samples, it achieves a Pearson correlation coefficient of 0.92 for continuous SDF regression and 89% accuracy for binary SDF classification—significantly outperforming existing nondestructive assessment techniques. This work establishes a novel paradigm for precise, noninvasive selection of high-quality sperm in IVF workflows.
📝 Abstract
Globally, infertility rates are increasing, with 2.5% of all births being assisted by in vitro fertilisation (IVF) in 2022. Male infertility is the cause for approximately half of these cases. The quality of sperm DNA has substantial impact on the success of IVF. The assessment of sperm DNA is traditionally done through chemical assays which render sperm cells ineligible for IVF. Many compounding factors lead to the population crisis, with fertility rates dropping globally in recent history. As such assisted reproductive technologies (ART) have been the focus of recent research efforts. Simultaneously, artificial intelligence has grown ubiquitous and is permeating more aspects of modern life. With the advent of state-of-the-art machine learning and its exceptional performance in many sectors, this work builds on these successes and proposes a novel framework for the prediction of sperm cell DNA fragmentation from images of unstained sperm. Rendering a predictive model which preserves sperm integrity and allows for optimal selection of sperm for IVF.