Expert-level aspiration and penetration detection during flexible endoscopic evaluation of swallowing with artificial intelligence-assisted diagnosis

Abstract

Abstract Flexible endoscopic evaluation of swallowing (FEES) is considered the gold standard in diagnosing oropharyngeal dysphagia. Recent advances in deep learning have led to a resurgence of artificial intelligence-assisted computer-aided diagnosis (AI-assisted CAD) for a variety of applications. AI-assisted CAD would be a remarkable benefit in providing medical services to populations with inadequate access to dysphagia experts, especially in aging societies. This paper presents an AI-assisted CAD named FEES-CAD for aspiration and penetration detection on video recording during FEES. FEES-CAD segments the input FEES video and classifies penetration, aspiration, residue in the vallecula, and residue in the hypopharynx based on the segmented FEES video. We collected and annotated FEES videos from 199 patients to train the network and tested the performance of FEES-CAD using FEES videos from other 40 patients. These patients consecutively underwent FEES between December 2016 and August 2019 at Fukushima Medical University Hospital. FEES videos were deidentified, randomized, and rated by FEES-CAD and laryngologists with over 15 years of experience in performing FEES. FEES-CAD achieved an average Dice similarity coefficient of 98.6 $$\%$$ % . FEES-CAD achieved expert-level accuracy performance on penetration (92.5 $$\%$$ % ), aspiration (92.5 $$\%$$ % ), residue in the vallecula (100 $$\%$$ % ), and residue in the hypopharynx (87.5 $$\%$$ % ) classification tasks. To the best of our knowledge, FEES-CAD is the first CNN-based system that achieves expert-level performance in detecting aspiration and penetration.