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Technical and engineering sciences

Andreas Kist







Universitätsklinikum Erlangen




Andreas Kist studied Molecular Medicine at the Friedrich-Alexander-University (FAU) Erlangen-Nürnberg. During his studies, Andreas has been involved in innovate healthcare projects at Siemens AG. He received his PhD in Neuroscience for his work at the Max-Planck-Institute for Neurobiology in Martinsried (Munich). Since 2018, Andreas has been working as postdoc at the University Hospital, Department of Otorhinolaryngology, Head&Neck surgery, in Erlangen. In a BMWi-funded project, his aim is to bring laryngeal high-speed videoendoscopy to the clinic. Specifically, Andreas is transforming the complex image processing pipeline using AI tools. Andreas will be a junior faculty member at FAU in April 2021.

Andreas presented his interdisciplinary research on numerous national and international conferences, and received several awards for his works, for example from the Boehringer Ingelheim Fonds or the Joachim-Herz foundation. Andreas is married and father of two wonderful girls.

Questions with regard to Artificial Intelligence

Laryngeal high-speed videoendoscopy (HSV) is an excellent tool to quantify the vocal fold oscillation, important to determine the patient’s health status and to monitor treatment progress. Despite the great advantages of HSV compared to current clinical tools, HSV is barely used in the clinic due to the complex and manual data analysis, and outdated hardware. Therefore, a clinically applicable HSV system should provide a fully automatic data analysis and latest hardware.

Andreas lead an international collaboration of several hospitals and research institutions to create an open multihospital dataset for glottis segmentation, crucial for any further HSV data analysis. With this, Andreas was able to develop novel, highly efficient and generalized deep neural networks (DNNs) that can be deployed to inexpensive hardware accelerators. Andreas’ interdisciplinary research is focused on developing and evaluating clinical-applicable semantic segmentation and health status classification DNNs.