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通过人工智能驱动的呼吸学推进结肠直肠癌诊断:应对挑战和未来方向

Ioannis K Gallos¹, Dimitrios Tryfonopoulos¹, Gidi Shani²

¹Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece.

Diagnostics (Basel, Switzerland)

|December 22, 2023

概括

呼吸学为早期结直肠癌检测提供了一种非侵入性方法. 使用机器学习分析呼吸中的挥发性有机化合物显示了改善查和患者舒适性的承诺.

科学领域:

在瘤学瘤学.
分析化学分析化学
生物信息学是一种生物信息学.

背景情况:

早期发现结直肠癌对于改善患者的治疗结果和降低死亡率至关重要.
目前的查方法有局限性,包括由于不适而导致的患者吸收率低,阻碍了早期诊断.
新型,非侵入性查替代品是重要的研究重点.

研究的目的:

对呼吸学进行非侵入性结肠直肠癌检测的研究进行审查.
专注于机器学习应用程序,用于分析癌症查中的呼吸学数据.
确定在结直肠癌的呼吸学中对人工智能的挑战和未来方向.

主要方法:

使用出口气中存在的挥发性有机化合物 (VOC).
应用机器学习算法来分析复杂的呼吸学数据.
在欧洲ONCOSCREEN项目框架内探索呼吸分析的潜力.

主要成果:

呼吸学为非侵入性癌症检测和监测提供了一个有前途的途径.
机器学习对于解释呼吸中的VOC复杂模式至关重要.
呼吸分析的进步可能会显著破坏当前结肠直肠癌查实践.

结论:

呼吸学与机器学习相结合,为早期发现结直肠癌提供了一种可行的非侵入性策略.
解决人工智能应用中的挑战是实现呼吸学全部潜力的关键.
未来的研究方向,包括ONCOSCREEN项目,旨在完善这些方法用于临床应用.

关键词:

在屏幕上播放.人工智能的人工智能是人工智能.自动化诊断自动化诊断呼吸学 (breathomics) 是一种呼吸学.结肠直肠癌是什么意思机器学习是机器学习.多元学习学习多元学习验证验证的时间挥发性有机化合物挥发性有机化合物

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