开发和验证一个人工智能驱动的工具来评估功能:一个概念验证
Anastasios Grigoriadis1, Soroush Baseri Saadi2, Linda Munirji1
1Department of Dental Medicine, Karolinska Institutet, Huddinge, Sweden.
Journal of dentistry
|December 15, 2024
概括
使用YOLOv8的新人工智能工具通过分析食物碎片来准确评估功能,提供了一种可靠的性能临床评估方法.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 口腔健康研究研究 口腔健康研究
背景情况:
- 功能对口腔和整体健康至关重要.
- 对功能的客观评估仍然是一个临床挑战.
- 目前用于评估口功能的现有方法往往在临床上是不可行的.
研究的目的:
- 开发和验证一个用于评估功能的自动化工具.
- 建立人工智能驱动的器性能分析的概念验证.
- 为客观临床评估能力创造一个可行的解决方案.
主要方法:
- 利用YOLOv8,一个深层神经网络,用于食物碎片检测和细分.
- 使用边界框回忆,细分指标和混矩阵评估模型性能.
- 采用布兰德-阿尔特曼图表来评估物理单位的细分性能.
主要成果:
- YOLOv8模型在检测食物碎片方面实现了超过90%的回忆和灵敏度.
- 在316个基本真理碎片中成功分类了301个,准确度很高.
- 布兰德-阿尔特曼分析表明普遍一致,碎片大小测量略高估计.
结论:
- 人工智能为自动化器性能分析提供了可靠的方法.
- 开发的AI工具是未来对功能临床评估的宝贵资产.
- 这项研究表明了人工智能在推进对能力的客观评估方面的潜力.
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