用机器学习从精子图像预测DNA断裂，无损筛选优质精子
Predicting DNA fragmentation: A non-destructive analogue to chemical assays using machine learning
- 基于未染色精子显微图像，构建预测DNA断裂的机器学习模型
- 可避免传统化学检测对精子的破坏，保留其用于IVF的潜力
- 为辅助生殖技术提供无损、高效、精准的精子质量评估方案
全球不孕率持续上升，2022年约2.5%的出生依赖体外受精（IVF），其中男性因素占近一半。精子DNA质量对IVF成功率影响显著。传统评估依赖化学检测，会破坏精子，使其无法用于后续IVF。随着辅助生殖技术（ART）成为研究重点，人工智能在各领域广泛应用。本文提出一种新框架，利用未染色精子图像，通过机器学习预测精子DNA断裂程度，实现无损评估，保留精子完整性，支持更优的IVF精子选择。
原文摘要 · Abstract (English)
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.
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