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