推进医疗保健:将生物传感器和机器学习协同用于早期癌症诊断
Mahtab Kokabi1, Muhammad Nabeel Tahir1, Darshan Singh1
1Department of Electrical and Computer Engineering, Rutgers the State University of New Jersey, Piscataway, NJ 08854, USA.
Biosensors
|September 27, 2023
概括
早期发现癌症至关重要. 本综述强调了先进的生物传感器和机器学习 (ML) 算法如何改善护理点诊断,以更快,更实惠地识别癌症.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 癌症仍然是导致死亡的主要原因,由于传统方法的局限性,癌症往往被诊断为晚期.
- 早期检测对于有效治疗至关重要,需要易于使用和快速的诊断工具.
- 护理点 (POC) 生物传感器为及时诊断癌症提供了一个有希望的替代方案.
研究的目的:
- 审查POC癌症诊断生物传感器的新兴技术.
- 探索机器学习 (ML) 算法在处理生物传感器数据中的作用.
- 为了比较不同ML算法的性能和癌症分类的传感方式.
主要方法:
- 审查POC生物传感器技术和ML应用的最新进展.
- 分析ML算法用于生物传感器数据解释的统计技术.
- 基于分类准确性的ML算法和传感平台的比较评估.
主要成果:
- 微型化和降低成本使POC生物传感器在癌症诊断方面变得更加实用.
- 机器学习算法增强生物传感器的数据处理,提取有价值的诊断信息.
- 性能指标,包括分类准确性,在不同的ML算法和传感方式之间有所不同.
结论:
- 与ML集成的POC生物传感器显示了早期和可访问的癌症诊断的巨大潜力.
- 持续开发ML和生物传感器技术对于推进癌症检测策略至关重要.
- 优化ML算法和传感平台可以提高诊断准确度和患者的治疗结果.
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