通过使用自主监督学习来智能识别医疗和兽医细胞内原生动物
Veerayuth Kittichai1, Morakot Kaewthamasorn2, Naruchit Soiphet3
1Faculty of Medicine, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand.
Parasites & vectors
|February 10, 2026
概括
人工智能 (AI) 使用无标签蒸版本2 (DiNOv2) 准确识别血液样本中的原生动物. 这种自动化工具通过减少对手工显微镜检查的依赖,有助于诊断,特别是在资源有限的环境中.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 医学诊断 医学诊断 医学诊断
- 寄生虫学的寄生虫学
背景情况:
- 动物传播疾病对全球健康构成重大挑战.
- 准确诊断原生动物感染对于有效控制至关重要.
- 目前的诊断方法,如显微镜和分子技术有局限性,包括需要熟练的人员和潜在的变化.
研究的目的:
- 开发和评估一种自主监督学习 (SSL) 方法,用于从Giemsa染色血液样本中自动提取原生动物的特征.
- 为此目的使用无标签蒸2版本 (DiNOv2) 算法.
主要方法:
- 开发SSL模型,包括DiNOv2,使用存档的微生物样本.
- 评估模型性能与基线视觉变压器 (ViT) 相比.
主要成果:
- DiNOv2-Small实现了超过99%的准确性和特异性,错误分类率低 (0.263) 和高AUC (0.990).
- 即使只对20%的数据进行训练,SSL模型也保持了高性能.
- 当数据集大小减少到20%以下时,性能指标显著下降.
结论:
- 由人工智能驱动的SSL方法显示了协助实验室技术人员的潜力,特别是在资源有限的地区.
- 这些发现支持在医学和兽医诊断中部署这款人工智能工具进行自动查.
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