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

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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.