Predicting DNA fragmentation: A non-destructive analogue to chemical assays using machine learning
It addresses male infertility by providing a non-destructive alternative to chemical assays, which is incremental as it applies existing ML methods to a new domain.
This work tackles the problem of assessing sperm DNA fragmentation for IVF by developing a machine learning framework that predicts fragmentation from unstained sperm images, enabling non-destructive selection and preserving sperm integrity.
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.