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一个深度学习管道用于在灵活的喉腔镜中自动分类声的息肉

Peter Yao¹, Dan Witte¹, Alexander German¹

¹Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, 240 East 59th St, New York, NY, 10022, USA.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

|September 11, 2023

概括

一个新的深度学习模型使用喉腔镜视频准确地区分声多 (VFP) 和健康的声多 (HVF). 在机器识别的框架上训练的模型表现优于在人类标记的框架上训练的模型.

科学领域:

耳鼻喉科耳鼻喉科耳鼻喉科
人工智能的人工智能
医疗成像医学成像

背景情况:

声多 (VFP) 是声的常见原因之一.
准确诊断VFP对于有效治疗至关重要.
深度学习模型在医学图像分析方面表现有前途.

研究的目的:

开发和验证一个深度学习模型,使用喉腔镜视频来区分健康的声 (HVF) 和VFP.
评估先前存在的信息框架分类器在简化深度学习模型开发中的实用性.
为了比较在机器标记与人类标记信息框架上训练的模型的性能.

主要方法:

对52例HVF和77例VFP病例的喉腔镜视频进行了回顾性分析.
使用经过验证的信息框架分类器提取信息框架.
训练两个基于ResNet18的深度学习模型来对HVF和VFP框架进行分类.
基于机器标记与人类标记数据集的模型性能 (精度和AUROC) 的比较.

主要成果:

在机器标记上训练的深度学习分类器实现了85%的准确性和0.84.8的AUROC.
在人类标记上训练的分类器实现了69%的准确性和0.66.6的AUROC.
机器标签显著提高了VFP分类模型的性能.

结论:

一个强大的深度学习分类器用于VFP识别成功开发和验证.
使用信息框架分类器为模型培训提供了有效的数据集组装.
在机器标记数据上训练的深度学习模型在VFP检测方面表现优异,而不是在人类标记数据上训练的模型.

关键词:

人工智能的人工智能是人工智能.计算机视觉计算机视觉计算机视觉卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.有关信息的框架.聚类的分类方法

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Larynx

Larynx

The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
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