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用机器学习软件和传统方法对牙表型评估进行比较评估 - - 一项横截面观测研究

Supriya S Kaule¹, Surekha R Rathod¹, Vibha R Bora²

¹Department of Periodontics and Implant Dentistry, Ranjeet Deshmukh Dental College and Research Centre, Nagpur, Maharashtra, India.

Journal of pharmacy & bioallied sciences

|October 30, 2025

概括

机器学习软件准确评估牙表型,与传统方法相匹配. 这种非侵入性技术为评估牙厚度和健康提供了可靠的替代方案.

科学领域:

牙科牙科是指牙科的专业.
牙周病学牙周病学
医疗保健中的人工智能

背景情况:

评估牙表型的传统方法存在局限性.
评估非侵入性技术对于准确的牙周病诊断至关重要.

研究的目的:

评估机器学习 (ML) 软件用于牙表型评估的有效性和准确性.
将ML软件与传统方法比较,例如探针透明度和跨探针探针.

主要方法:

一项涉及382名牙周健康患者的研究.
使用探针透明度,经牙探针和ML软件在上切口区域进行牙表型评估.

主要成果:

在三个评估方法之间没有发现统计学上显著的差异,表明它们的等价性.
与女性相比,男性表现出较高的厚牙表型的流行率.

结论:

机器学习软件,特别是使用卷积神经网络,对于识别牙表型是有效的.
ML软件为牙表型评估提供了一个可行的,准确的非侵入性替代方案.

关键词:

卷积神经网络是一种卷积神经网络.牙表现型牙表现型机器学习软件方法机器学习软件方法探测器透明度探测器透明度跨关节的探测探测

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