高通量成像细胞术联合人工智能识别临床无精子症男性中的极罕见精子
该回顾性队列研究利用高通量成像细胞术和AI辅助图像分析,检测了148份临床无精子症的精液样本。在83份可分析的无精子症样本中,40份(48%)被重新分类为存在精子。作者据此提出「微精子症」的新分类,指精子浓度极低、仅先进图像搜索技术可可靠识别的状态。
要点
- 高通量成像细胞术联合AI可在临床无精子症样本中识别极罕见精子。
- 83份无精子症样本中48%被检出精子,提示部分临床无精子症可能被重新分类。
- 提出「微精子症」概念,指需先进图像技术才能可靠识别的极低精子浓度。
核心结论
- 高通量成像细胞术联合AI图像分析可在临床判定为无精子症的精液样本中识别极罕见精子,并据此提出「微精子症」新分类。
主要发现
- 在83份临床无精子症样本中,40份(48%)经该平台检出精子。
关键数值与推荐
- 以CNN形态学评估和精子特异性蛋白表达作为诊断再分类依据;83份无精子症样本中40份(48%)检出精子。
临床怎么用
- 对于临床诊断的无精子症患者,可考虑采用高通量成像细胞术联合AI辅助分析重新评估精液中是否存在极罕见精子,以指导后续生育决策。
局限性
- 研究为单中心回顾性设计,使用残余生物样本,样本量有限(148份,其中83份无精子症样本可分析)。
以上解读依据论文摘要(全文未开放获取或许可不允许翻译转载)。
原文
OBJECTIVE: To determine whether a novel diagnostic platform, which pairs high-throughput imaging cytometry with artificial intelligence (AI)-assisted image analysis, can identify ultrarare sperm in ejaculatory samples deemed clinically azoospermic. DESIGN: A retrospective cohort review of remnant biobanked ejaculatory samples processed by high-throughput imaging cytometry and AI-assisted image analysis. SUBJECTS: Remnant ejaculatory samples (n = 148) biobanked from December 2024 to September 2025 at a single high-volume male infertility clinic. EXPOSURE: Image-based flow cytometry and convolutional neural network (CNN) driven image analysis for diagnostic classification. MAIN OUTCOME MEASURES: Diagnostic reclassification rate of remnant ejaculatory samples initially deemed clinically azoospermic using CNN-based morphologic assessment and corollary sperm specific protein expression. RESULTS: Within the 83 azoospermic samples analyzed, 40 samples (48%) were found to have sperm using this high-fidelity diagnostic platform. CONCLUSION: We propose a new classification of sperm concentration, microzoospermia, a sperm concentration so low that only advanced image-based search technologies can reliably identify ultrarare sperm.
引用
- Stocks B, Boyne A, Oppenheimer A, et al. High-throughput imaging cytometry paired with artificial intelligence identifies ultrarare sperm in men previously diagnosed with clinical azoospermia. Fertil Steril. 2026. PMID: 42036054. doi:10.1016/j.fertnstert.2026.04.011
供执业医师工作参考,不替代临床判断;数值与分级以指南原文为准。