A Deep Learning Approach to Video Fluoroscopic Swallowing Exam Classification

Abstract

Dysphagia, or difficulty swallowing, is a serious health problem that reduces the quality of life of those affected. The standard method to diagnose dysphagia is the x-ray video fluoroscopic swallowing exam (VFSE). In this paper we investigate the use of deep learning networks to classify VFSE as normal or abnormal. The proposed network is based on a long term recurrent convolutional network (LRCN). This network was trained and validated using 1154 VFSE. Using 10-fold cross-validation, the accuracy of classification was 85% and the area under the ROC curve was 0.89. This work shows the promise of using deep learning networks as a screening tool to detect dysphagia in VFSE.