Artificial Intelligence-Driven Penetration-Aspiration Detection in Dysphagia Patients Using Fluoroscopic Videos

Sanjeevi G

Keywords: Dysphagia, Airway detection, Bolus segmentation, Deep learning

TL;DR: The article propose an AI decision support system including bolus segmentation and airway detection for diagnosing penetration-aspiration using Videofluoroscopic Swallowing Study in dysphagia patients.

Abstract: The gold standard for diagnosing dysphagia is the Videofluoroscopic Swallowing Study (VFSS). In patients with dysphagia, the invasion of food material into the airway is known as penetration-aspiration. Assessing this risk using VFSS is inherently subjective, with significant inter-patient and inter-rater variability. This article proposes an AI pipeline that introduces a novel approach in which bolus segmentation and airway detection are combinationally assessed to interpret frame-wise penetration-aspiration risk. The existing AI approaches rely on manual frame selection and overlook the clinical significance of bolus and airway. Additionally, addressing challenges posed by varying airway orientations, we develop an automated AI pipeline that tracks bolus and airway throughout VFSS videos. We curated a VFSS dataset and annotated one-third of the frames from 82 VFSS clips obtained from 40 patients due to a lack of benchmarks. Our approach involved comparing various segmentation models for bolus segmentation and fine-tuning object detection model for airway detection. The segmented bolus area and airway information are then processed to identify penetration-aspiration events. Our pipeline achieved a dice score of 0.80, a mean average precision of 0.93, and an accuracy of 89% in bolus segmentation, airway detection, and penetration-aspiration detection. Our pipeline could be effectively trained even with limited annotated frames. This saved clinicians time and also reduced the burden of manual annotation. These promising results have significant potential for assisting clinicians in assessing penetration-aspiration risk.

Track: 4. AI-based clinical decision support systems

Registration Id: MWNVW7TC7SF

Submission Number: 325