用AI从显微镜图像预测精子DNA损伤，无需破坏样本。
Validation of an Artificial Intelligence Tool for the Detection of Sperm DNA Fragmentation Using the TUNEL In Situ Hybridization Assay
- 结合形态分析与Transformer模型，构建集成学习框架
- 灵敏度60%，特异性75%，可实时判断精子DNA完整性
- 适合辅助生殖临床诊断，尤其需精准选精的场景
精子DNA碎片化（SDF）是男性生育力评估的关键指标，传统精液分析无法检测。本研究验证了一种新型人工智能工具，通过数字分析相位对比显微镜图像，利用末端脱氧核苷酸转移酶介导的dUTP缺口末端标记（TUNEL）法作为金标准，检测SDF。基于精子形态与DNA完整性之间的关联，提出一种形态辅助的集成学习AI模型，融合图像处理技术与先进的基于Transformer的机器学习模型（GC-ViT），用于从相位对比图像中预测精子的DNA碎片化程度。该模型与纯Transformer视觉模型及仅依赖形态的模型进行对比。结果显示，所提框架在识别上达到60%的灵敏度和75%的特异性。此非破坏性方法在生殖医学领域具有重要意义，可实现基于DNA完整性的实时精子筛选，适用于临床诊断与治疗应用。
原文摘要 · Abstract (English)
Sperm DNA fragmentation (SDF) is a critical parameter in male fertility assessment that conventional semen analysis fails to evaluate. This study presents the validation of a novel artificial intelligence (AI) tool designed to detect SDF through digital analysis of phase contrast microscopy images, using the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay as the gold standard reference. Utilising the established link between sperm morphology and DNA integrity, the present work proposes a morphology assisted ensemble AI model that combines image processing techniques with state-of-the-art transformer based machine learning models (GC-ViT) for the prediction of DNA fragmentation in sperm from phase contrast images. The ensemble model is benchmarked against a pure transformer `vision' model as well as a `morphology-only` model. Promising results show the proposed framework is able to achieve sensitivity of 60\% and specificity of 75\%. This non-destructive methodology represents a significant advancement in reproductive medicine by enabling real-time sperm selection based on DNA integrity for clinical diagnostic and therapeutic applications.
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