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相关概念视频

Assessment of the Mouth01:26

Assessment of the Mouth

974
A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.
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A Real-time Electrical Impedance Based Technique to Measure Invasion of Endothelial Cell Monolayer by Cancer Cells
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基于电阻的分类,用于体内检测口腔癌.

Sophie A Lloyd, Noor Zaghlula, Safina S Suratwala

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    机器学习算法使用电阻测量在手术期间准确识别口腔癌组织. 这项技术有助于外科医生实现完整的瘤切除和负边缘,改善患者的治疗结果.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 在瘤学瘤学.
    • 机器学习 机器学习

    背景情况:

    • 口腔癌手术在区分癌症和健康组织方面面临挑战,影响完全切除和负边缘.
    • 术内电阻 (IE) 装置为实时组织分类提供了潜在的解决方案.
    • 机器学习 (ML) 算法的进步可以提高基于IE的癌症检测的准确性和速度.

    研究的目的:

    • 评估各种ML算法在识别口腔癌组织样本方面的性能.
    • 评估IE测量与ML相结合的疗效,以评估手术内边际.
    • 为了比较不同组织类型的二进制和多类分类性能.

    主要方法:

    • 在手术期间,从健康的,良性,失生性和癌性口腔组织中收集了手术内IE测量.
    • 应用了几种ML算法来分析IE数据用于组织分类.
    • 用曲线下的面积 (AUC) 度量来评估性能,用于二进制和多类设置.

    主要成果:

    • 在对癌症病变的二进制分类中实现了高精度,AUC达到0.92.2.
    • 在所有组织类型的多类分类表现中,AUC值为0.81.
    • 这项研究表明了ML增强的IE测量的潜力,用于手术内检测口腔癌.

    结论:

    • ML算法在使用手术内IE测量准确识别口腔癌组织方面显示出显著的希望.
    • 这种方法可以帮助外科医生实现负边缘和完全切除瘤.
    • 为了临床应用,需要进一步优化和探索额外的ML模型.