Objective analysis of functional based hoarseness by clinical high-speed endoscopy

临床高速内镜客观分析功能性声音嘶哑

• 批准号: 323308998
• 负责人: Professor Dr.-Ing. Michael Döllinger
• 金额: --
• 依托单位: Hals-Nasen-Ohren-Klinik, Kopf- und Halschirurgie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/323308998?language=en
• 关键词: Objective; analysis; functional; based; hoarseness

Hoarseness based on functional dysphonia is rather common in daily clinical routine. According to statistics, functional dysphonias may state up to 50% of all diagnosed disordered voices. In contrast to easily to recognize morphological based voice disorders, functional dysphonias show their clinical picture only during phonation, i.e. during vibration. These pathological vibration patterns range from periodic slightly dynamic left-right asymmetry without glottal gap to highly dynamic left-right asymmetry or even irregular vibrations combined with a glottal gap. To visualize vocal fold dynamics, we perform endoscopic digital high-speed video imaging (HSV) and synchronously record the emitted acoustic voice signal. So far, HSV based diagnostics is still widely performed subjectively and therefore depends strongly on the experience of the medical doctor. Reasons therefore are still missing automated clinically applicable image processing algorithms and hence the lack of commonly accepted objective HSV parameters. Within the current project, so far, we developed a software tool allowing for automatic extraction of objective HSV parameters; i.e. sufficient fast, valid and robust. This software is already used by 27 research groups in 7 countries. We showed that many applied HSV parameters are not suitable for characterizing functional dysphonia and that many parameters are also not suitable for clinical application at all. Applying state-of-the-art machine learning algorithms we separated different kinds of functional dysphonia and separated these also from healthy voice production. However, the current accuracy is not yet sufficient for clinical use, since the classification tasks were performed separately for the different sensor data: acoustics, HSV imaging and clinical assessment tools. We will overcome this shortcoming in the next project phase:Hence, the central goal in this project phase is a combined analysis of all multi-sensor (acoustics, HSV imaging, further clinical assessment tools) data by applying machine learning techniques to (1) objectively grade hoarseness; (2) determine age dependent parameters; (3) objectively assess and quantify treatment progress; (4) implement the developed machine learning algorithms in a software tool that then can be used by other research and clinical groups to finally transfer these machine learning methods to clinical application; i.e. a computer based quantitative and visual presentation of the clinical status for assessment of the clinical picture of functional dysphonia and treatment progress.

功能性发声障碍引起的声嘶在临床上相当常见。据统计，功能性发声障碍可能占所有诊断的声音障碍的50%。与容易识别的基于形态学的语音障碍相反，功能性发音障碍仅在发声期间（即，在振动期间）显示其临床图片。这些病理性振动模式的范围从没有声门间隙的周期性轻微动态的左右不对称到高度动态的左右不对称，甚至是与声门间隙相结合的不规则振动。为了可视化声带动力学，我们进行内窥镜数字高速视频成像（HSV），并同步记录发出的声学语音信号。到目前为止，基于HSV的诊断仍然广泛地主观地进行，因此强烈地依赖于医生的经验。因此，原因仍然是缺少自动临床适用的图像处理算法，因此缺乏普遍接受的客观HSV参数。在目前的项目中，到目前为止，我们开发了一个软件工具，可以自动提取目标HSV参数;即足够快，有效和强大。该软件已被7个国家的27个研究小组使用。我们发现，许多应用HSV参数不适合表征功能性发声障碍，许多参数也不适合临床应用。应用最先进的机器学习算法，我们将不同类型的功能性发声障碍与健康的声音产生分开。然而，目前的准确度还不足以用于临床使用，因为分类任务是针对不同的传感器数据单独执行的：声学、HSV成像和临床评估工具。我们将在下一个项目阶段克服这个缺点：因此，这个项目阶段的中心目标是对所有多传感器进行组合分析。通过应用机器学习技术来获得（声学、HSV成像、进一步的临床评估工具）数据，以（1）客观地对声音嘶哑进行分级;（2）确定年龄依赖性参数;（3）客观地评估和量化治疗进展;（四）在软件工具中实现所开发的机器学习算法，然后其他研究和临床小组可以使用该软件工具来最终将这些机器将学习方法应用于临床;即基于计算机的临床状态的定量和视觉呈现，用于评估功能性发声障碍的临床表现和治疗进展。