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深度学习技术的应用,用于视频光学吞研究的时间分析

Seong Yun Jeong¹, Jeong Min Kim², Ji Eun Park²

¹Department of Computer Science and Engineering, Korea University, 145 Anam-ro Seongbuk-gu, Seoul, 02841, Korea.

Scientific reports

|October 16, 2023

概括

本研究引入了一种深度学习模型,用于自动测量视频光学吞研究 (VFSS) 的时间吞参数. 与手工方法相比,AI模型显著提高了VFSS分析的准确性和效率.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
吞障碍诊断诊断吞障碍

背景情况:

在视频光学吞研究 (VFSS) 中,吞功能的客观和定量评估至关重要.
手动分析VFSS时间参数是劳动密集的,复杂的,容易出现人为错误.
使用深度学习的自动化分析为改善VFSS解释提供了潜在的解决方案.

研究的目的:

开发和评估一个深度学习模型,用于自动测量时间吞参数.
将拟议的深度学习模型与现有方法 (VGG,I3D) 的性能进行比较.

主要方法:

使用了547个视频光学吞研究 (VFSS) 视频片段.
手动测量了七个关键的时间参数 (口腔阶段持续时间,喉延迟时间等). 由两位物理医生作为基本事实.
采用ResNet3D作为时间参数分析的基础深度学习模型,将其变体与VGG和I3D模型进行比较.

主要成果:

拟议的ResNet3D变种实现了高平均精度,从0.901到0.981.
F1分数和平均精度值令人印象深刻,分别从0.794到0.941和0.714到0.899不等.
开发的ResNet3D模型在精度,F1分数和平均精度方面表现优于VGG和I3D模型.

结论:

深度学习模型在自动测量来自VFSS的时间吞参数方面表现出卓越的性能.
这种自动化方法有望使VFSS时间分析在临床应用中更容易获得,更有效,更准确.
这些发现表明,使用人工智能对吞功能的客观评估取得了重大进展.

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