预测DNA碎片化：一种使用机器学习的非破坏性化学检测替代方法
Predicting DNA fragmentation: A non-destructive analogue to chemical assays using machine learning
AI总结：
针对传统化学检测评估精子DNA碎片化会使精子失去IVF资格的问题，本研究提出基于机器学习从无染色精子图像预测DNA碎片化的框架，构建保持精子完整性的预测模型，助力辅助生殖技术（ART）发展。
摘要：
全球范围内，不孕率正在上升，2022年所有新生儿中2.5%通过体外受精（IVF）辅助出生。男性不孕约占这些病例的一半。精子DNA质量对IVF成功率有重大影响。传统上，精子DNA评估通过化学检测进行，这会使精子细胞失去IVF资格。许多复合因素导致人口危机，近年来全球生育率持续下降。因此，辅助生殖技术（ART）成为近期研究重点。同时，人工智能已变得无处不在，正渗透到现代生活的更多方面。随着最先进的机器学习的出现及其在许多领域的卓越表现，本研究基于这些成功，提出一种从未染色精子图像预测精子细胞DNA碎片化的新型框架，构建了一个能保持精子完整性、允许最优选择IVF用精子的预测模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
