# Development and Validation of an Artificial Intelligence-Based Clinical Decision Support Tool for Videofluoroscopic Swallowing Studies

> **NIH NIH R21** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $187,715

## Abstract

ABSTRACT
Dysphagia (swallowing dysfunction) is highly prevalent in a variety of medical conditions and prevalence
increases with advancing age. If incorrectly diagnosed or left untreated, dysphagia can lead to serious health
consequences, including malnutrition, dehydration, and pneumonia. The most commonly used procedure to
diagnose dysphagia is the videofluoroscopic swallow (VFS) study. A VFS study utilizes barium to provide a
contrast enhanced fluoroscopic procedure that allows for visualization of anatomy and physiology relevant to
swallowing as well as identification of swallowing biomechanical impairments. Current VFS analysis methods
used clinically are primarily qualitative in nature and subject to issues with reliability. Quantitative methods to
support VFS clinical interpretation do exist but are primarily found in the research environment due to the time-
consuming nature of frame by frame analysis required. The overall objective of this application is to develop
and validate an artificial neural network-based software that will segment and track clinically important
swallowing structures on a frame-by-frame basis within swallowing videos. Segmentation and tracking will
automatically occur post acquisition with no needed input or video editing. Frame by frame auto-segmentation
of regions of interest will allow for quantitative metrics to be determined algorithmically. To accomplish this
objective, two specific aims are proposed: 1) to develop and validate an AI based auto-segmentation algorithm
that accurately segments swallowing anatomy and bolus flow in VFS studies from a retrospective cohort of
stroke and mixed etiology patients and 2) to apply the auto-segmentation algorithm to derive a variety of
clinically relevant metrics in VFS studies and compare to manually derived reference values. To accomplish
the first aim, pre-processing techniques will be established to improve image quality and reduce image artifacts
using a novel 3D printed anthropomorphic head & neck phantom. Using a robust existing dataset of VFS
images, we will then develop a Mask R-Convolutional Neural Network for automatic segmentation of a variety
of clinically relevant features on VFS studies and will validate the auto-segmentation against manually derived
segmentation. For the second aim, the auto-segmentation algorithm will be applied to derive important
swallowing measures and associated metrics from the VFS images. The output of the algorithm will be
validated against measures and metrics manually derived from the VFS images by experienced raters with
established reliability. Tools developed through this project will reduce the subjectivity of human interpretation
of VFS images, which will improve consistency and reliability of dysphagia diagnosis and treatment.

## Key facts

- **NIH application ID:** 10511906
- **Project number:** 1R21EB033618-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Bryan Patrick Bednarz
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $187,715
- **Award type:** 1
- **Project period:** 2022-08-08 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10511906

## Citation

> US National Institutes of Health, RePORTER application 10511906, Development and Validation of an Artificial Intelligence-Based Clinical Decision Support Tool for Videofluoroscopic Swallowing Studies (1R21EB033618-01). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10511906. Licensed CC0.

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