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Updated: Jun 18, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

通过使用深度学习算法从喉图像中识别性别.

Hiroshi Yoshihara¹, Memori Fukuda², Takaya Hanawa²

¹Aillis, Inc., Yaesu Central Tower 7F, 2-2-1 Yaesu, Chuo-ku, Tokyo, 104-0028, Japan. hiroshi.yoshihara@aillis.jp.

Scientific reports

|August 2, 2024

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此摘要是机器生成的。

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使用条款与条件

人工智能现在可以从喉图像中识别性别,类似于视网膜扫描. 这种深度学习模型显示了使用喉图像进行非侵入性性别识别的潜力.

科学领域:

医疗成像医学成像
人工智能的人工智能
耳鼻喉科耳鼻喉科耳鼻喉科

背景情况:

喉提供了一个独特的,非侵入性的窗口,用于观察身体内部结构,如血管和免疫组织.
之前的研究成功地使用人工智能 (AI) 来从视网膜图像中确定性别,但对喉膜图像的应用尚未探索.

研究的目的:

通过深度学习分析喉图像,研究识别个体性别的可行性.
开发和验证基于非侵入性获取的喉图像的性别分类人工智能模型.

主要方法:

一个使用多个实例卷积神经网络的深度学习分类模型,在来自日本51家初级保健诊所的20319张喉图像上进行了训练.
模型验证是在13个独立诊所的4,869张图像上进行的.
模型解释涉及一个框架,将突出和器官细分地图结合起来,以确定关键的图像区域.

主要成果:

人工智能模型实现了0.883的接收器操作特征曲线 (AUC) 下的面积 (95%CI 0.866-0.900),表明了准确的性别分类.
20岁及以上的个体的表现显著改善,这表明喉中与年龄相关的性别特异性模式.
度分析显示,该模型主要集中在后壁和上,用于分类.

结论:

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用深度学习算法分析的喉图像显示了准确和非侵入性性别识别的巨大潜力.
这些发现表明,人工智能驱动的喉图像分析可能是一个新的诊断或人口统计工具.
进一步的研究可能会探索从喉图像中识别性别的临床应用,特别是在老年人群中.