人工智能驱动的显微镜:转变寄生虫学的景观
Mariana De Niz1,2, Sara Silva Pereira3, David Kirchenbuechler1,2
1Center for Advanced Microscopy and Nikon Imaging Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Journal of microscopy
|June 10, 2025
概括
人工智能 (AI) 为寄生虫学研究挑战提供了强大的解决方案,增强了寄生虫识别和生命周期分析. 未来的人工智能集成有望在诊断方面取得进步,并采取统一的"一个健康"方法.
科学领域:
- 寄生虫学的寄生虫学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 显微镜和图像分析对于识别寄生虫和了解它们的生命周期至关重要.
- 当前的挑战包括整合跨学科数据,多样化的模型生物和临床研究结果.
- 人工智能 (AI),特别是机器学习 (ML) 和深度学习 (DL),具有克服这些障碍的巨大潜力.
研究的目的:
- 审查AI,ML和DL在寄生虫学中的应用,重点关注Apicomplexan,Diplomonad和Kinetoplastid群体.
- 探索人工智能如何填补未来寄生虫学研究和诊断方面的知识空白.
- 讨论在生物医学领域实施人工智能的当前挑战和局限性.
主要方法:
- 在寄生虫学中对AI,ML和DL应用的文献综述.
- 分析AI在处理寄生生物研究中的数据集成和复杂性方面的作用.
- 检查AI对科学发现和寄生虫学临床应用的潜在影响.
主要成果:
- 人工智能,ML和DL在寄生虫学中越来越多地用于诸如寄生虫识别和生命周期阐明等任务.
- 人工智能可以弥合理解的差距,并提高对寄生虫疾病的诊断能力.
- 实施挑战包括数据标准化,计算资源和跨学科合作.
结论:
- 人工智能对寄生虫学研究,诊断和扩展One Health原则具有变革性的潜力.
- 生物学家和计算科学家之间的加强合作对于实现人工智能的全部潜力至关重要.
- 未来的人工智能工具准备彻底改变寄生虫学,从而带来重大科学和临床进展.
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