Efficient Patient Orientation Detection in Videofluoroscopy Swallowing Studies

Luisa Neubig, René Groh, Melda Kunduk, Deirdre Larsen, Rebecca Leonard, Andreas M. Kist

Abstract: Swallowing disorders are commonly examined using videofluoroscopy swallowing studies (VFSS). To comprehensively evaluate the swallowing process, a typical VFSS contains different patient orientations. In order to quantify the swallowing physiology, a VFSS is systematically and temporally segmented for different patient orientations. However, no fully automatic temporal segmentation tool is available. Here, we show that in general multiple deep neural networks (DNNs) are suitable for this task. We found that a variety of optimization algorithms result in generalizing DNNs. Using a systematic architectural scaling approach, we found that an efficient ResNet18 variant is sufficient to classify a full VFSS recording of about 1800 frames in less than 14 s on conventional CPUs. In the future, our findings allow a successful clinical implementation.