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使用表式深度学习检测口腔癌

Zhiyun Xue¹, Zhaohui Liang¹, Sivaramakrishnan Rajaraman¹

¹Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.

IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems

|September 11, 2025

概括

早期发现口腔癌至关重要. 这项研究探讨了用于分类癌症和癌前病变的表格数据的深度学习,显示了改善患者结果的有希望的结果.

科学领域:

生物医学是生物医学.
人工智能的人工智能
机器学习机器学习

背景情况:

口腔癌的存活率很低,因此需要早期检测才能有效治疗.
目前的诊断方法主要依赖于图像分析,使得表格式临床数据中的潜力未被探索.
使用结构化文本数据进行口腔癌预测的深度学习应用尚未得到充分研究.

研究的目的:

通过使用表式临床数据,研究深度学习模型在预测癌症和癌前口腔病变方面的有效性.
将两种深度学习方法的性能与口腔癌风险评估的传统算法进行比较.
分析模型的可解释性,并确定关键的预测特征.

主要方法:

从正在进行的口腔癌研究中利用了1791名患者的子集,专注于结构化文本 (表格) 临床数据.
实现并比较了两种不同的深度学习架构,这些架构是为表格数据设计的.
将深度学习模型与传统的机器学习算法进行比较,以确定分类的准确性.

主要成果:

所有评估的模型在持久测试组中都表现出强大的预测性能,达到超过0.6的尤登指数和超过0.9.9的曲线下面积 (AUC).
模型解释性分析显示,病变特征是预测口腔癌风险的关键因素.
这项研究证实了AI/ML在分析口腔癌检测临床表格数据方面的潜力.

结论:

对表格式临床数据应用的深度学习方法对早期检测口腔癌有显著的前景.
损伤特征被确定为区分癌症和癌前口腔病变的关键指标.
这项研究为在生物医学应用中应用AI/ML提供了宝贵的见解,特别是在口腔瘤学中.

关键词:

深度学习是一种深度学习.检查口腔癌的查方法口腔损伤的特征口腔损伤的特征表格式数据是表格式数据.

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