AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

Abstract

Disorders of swallowing often lead to pneumonia when material enters the airways (aspiration). Flexible Endoscopic Evaluation of Swallowing (FEES) plays a key role in the diagnostics of aspiration but is prone to human errors. An AI-based tool could facilitate this process. Recent non-endoscopic/non-radiologic attempts to detect aspiration using machine-learning approaches have led to unsatisfying accuracy and show black-box characteristics. Hence, for clinical users it is difficult to trust in these model decisions. Our aim is to introduce an explainable artificial intelligence (XAI) approach to detect aspiration in FEES. Our approach is to teach the AI about the relevant anatomical structures, such as the vocal cords and the glottis, based on 92 annotated FEES videos. Simultaneously, it is trained to detect boluses that pass the glottis and become aspirated. During testing, the AI successfully recognized the glottis and the vocal cords but could not yet achieve satisfying aspiration detection quality. While detection performance must be optimized, our architecture results in a final model that explains its assessment by locating meaningful frames with relevant aspiration events and by highlighting suspected boluses. In contrast to comparable AI tools, our framework is verifiable and interpretable and, therefore, accountable for clinical users.

The Development of an Artificial Intelligence Video Analysis-Based Web Application to Diagnose Oropharyngeal Dysphagia: A Pilot Study

“…However, there are few studies that analyze video images with AI and use them to diagnose diseases. Konradi et al [24] developed explainable artificial intelligence (XAI) to analyze Flexible Endoscopic Evaluation of Swallowing (FEES) videos. In this pilot study, it was reported that the accuracy of the training data was 0.925 and the testing data was 0.571 to diagnose swallowing disorders [24].…”

“…Konradi et al [24] developed explainable artificial intelligence (XAI) to analyze Flexible Endoscopic Evaluation of Swallowing (FEES) videos. In this pilot study, it was reported that the accuracy of the training data was 0.925 and the testing data was 0.571 to diagnose swallowing disorders [24]. Similar to this study, Jeong et al [25] attempted to diagnose swallowing disorders using VFSS video with the ResNet3D AI model.…”