Predicting DNA fragmentation: A non-destructive analogue to chemical assays using machine learning

TL;DR

This paper introduces a machine learning framework to predict sperm DNA fragmentation from unstained sperm images, offering a non-destructive alternative to chemical assays for improving IVF success rates.

Contribution

It presents a novel, non-destructive machine learning approach to assess sperm DNA quality directly from images, enhancing fertility diagnostics without damaging sperm cells.

Findings

Achieved accurate prediction of DNA fragmentation from sperm images

Demonstrated preservation of sperm integrity during assessment

Potential to improve IVF success by better sperm selection

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.