一个基于网络的人工智能系统,用于无标签的病毒分类和检测细胞病变效应
Zeynep Akkutay-Yoldar1, Mehmet Türkay Yoldar2,3, Yiğit Burak Akkaş2
1Department of Virology, Faculty of Veterinary Medicine, Ankara University, Ankara, 06070, Turkey. zeynepakkutay@gmail.com.
Scientific reports
|February 18, 2025
概括
我们开发了一个人工智能系统,AIRVIC,用于在细胞培养物中自动检测病毒. 这种人工智能工具简化了病毒感染的识别,提高了全球研究人员的诊断效率.
科学领域:
- 病毒学 病毒学
- 人工智能的人工智能
- 细胞生物学 细胞生物学
背景情况:
- 传统的病毒识别方法是劳动密集型的,需要专门的专业知识.
- 细胞病变效应 (CPE) 是细胞培养中的病毒感染的视觉指标.
- 需要用于病毒诊断的自动化,高效的工具.
研究的目的:
- 开发和验证一个由人工智能驱动的自动化系统,AIRVIC,用于检测和分类由各种病毒引起的无标签细胞病变效应 (CPE).
- 评估AIRVIC在不同细胞系 (Vero,MDBK) 中识别特定病毒菌株 (SARS-CoV-2,BAdV-1,PIV3,BoAHV-1,BoGHV-4) 的性能.
- 建立AIRVIC作为病毒学诊断和抗病毒研究的新工具.
主要方法:
- 使用卷积神经网络开发AIRVIC,使用ResNet50架构.
- 在一个由40369张感染细胞培养的显微镜图像组成的数据集上训练AI模型.
- 测试AIRVIC在CPE检测和不同病毒菌株和细胞系的病毒分类中的准确性.
主要成果:
- 艾尔维克在MDBK细胞中检测到一种特定的Bovine alphaherpesvirus 4 (BoGHV-4) 菌株的准确性达到了100%.
- 该系统对不同病毒-细胞系组合的准确性各不相同,Vero细胞中BoGHV-4的最低准确率为87.99%.
- 牛病毒分类的多类精度在MDBK细胞中达到87.61%,在没有细胞系特征的情况下降至63.44%.
结论:
- 艾尔维克是第一个用于在细胞培养中区分动物病毒感染的AI应用程序,提供无偏见的感染性评分.
- 人工智能系统简化了病毒隔离,并促进了抗病毒疗效测试.
- AIRVIC可以作为一个基于网络的平台,使病毒诊断研究人员能够在全球范围内访问.
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