Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning

Abstract

Objectives: A videofluoroscopic swallowing study (VFSS) is conducted to detect aspiration. However, aspiration occurs within a short time and is difficult to detect. If deep learning can detect aspirations with high accuracy, clinicians can focus on the diagnosis of the detected aspirations. Whether VFSS aspirations can be classified using rapid-prototyping deep-learning tools was studied. Methods: VFSS videos were separated into individual image frames. A region of interest was defined on the pharynx. Three convolutional neural networks (CNNs), namely a Simple-Layer CNN, Multiple-Layer CNN, and Modified LeNet, were designed for the classification. The performance results of the CNNs were compared in terms of the areas under their receiver-operating characteristic curves (AUCs). Results: A total of 18,333 images obtained through data augmentation were selected for the evaluation. The different CNNs yielded sensitivities of 78.8%–87.6%, specificities of 91.9%–98.1%, and overall accuracies of 85.8%–91.7%. The AUC of 0.974 obtained for the Simple-Layer CNN and Modified LeNet was significantly higher than that obtained for the Multiple-Layer CNN (AUC of 0.936) (p < 0.001). Conclusions: The results of this study show that deep learning has potential for detecting aspiration with high accuracy.