# Instrumental screening for dysphagia by combining high-resolution cervical auscultation with advanced data analysis tools to identify silent dysphagia and silent aspiration

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $302,394

## Abstract

ABSTRACT
Dysphagia (disordered swallowing) causes nearly 150,000 annual hospitalizations and over 220,000 additional
hospital days, and prolongs hospital lengths of stay by 40%. Dysphagia risk is typically identified through
subjective screening methods and those identified through screening undergo gold standard imaging testing
such as videofluoroscopy (VF). However, screening methods over- or underestimate risk, and completely fail to
identify patients with silent dysphagia (e.g., silent aspiration) that can cause pneumonia and other adverse
events. Pre-emptive detection of silent or near-silent aspiration is essential. The long term goal is to develop
an instrumental dysphagia screening approach based on high-resolution cervical auscultation (HRCA) in order
to early predict dysphagia-related adverse events, and initiate intervention measures to mitigate them. The
overall objective here is to develop accurate, advanced data analysis approaches to translate HRCA signals to
swallowing events observed in VF images. Our strong preliminary data has led us to our central hypothesis:
advanced data analytics tools are suitable approaches for the analysis of HRCA in order to automate
dysphagia screening. The rationale is that a reliable, robust early-warning instrumental dysphagia screening
approach will reduce adverse events in patients with silent aspiration/dysphagia, shorten length of stay and
improve overall clinical outcomes. Guided by strong preliminary data, we will pursue the following three specific
aims: (1) develop machine learning algorithms to differentiate HRCA signals produced by swallowing
physiologic events from similar, non-swallow related signals produced during swallowing; (2) translate HRCA
swallowing-signal signatures to actual swallow physiologic events to detect abnormal swallowing physiology;
and (3) discriminate normal from abnormal airway protection and swallow physiology via machine-learning
analysis of HRCA signals with similar accuracy as VF. Under the first aim, a machine learning approach will be
used to detect pharyngeal swallowing events and differentiate them from speech, cough and other non-
swallow events, with 90% accuracy, when compared to a human expert’s interpretation of our VF data sets.
Under the second aim, objective swallowing physiology observations from VF will be matched to swallowing
events observed with HRCA in order to show that abnormal swallow physiology and airway protection will
produce distinctive HRCA signal signatures that predict the same events identified with VF. Under the third
aim, analytical algorithms will be used to detect signs of disordered airway protection in HRCA signal
signatures with 90% accuracy when compared to a human expert’s airway protection ratings from VF images.
The approach is innovative, as it will produce analysis tools that will infer about dysphagia and aspiration
based on the analysis of HRCA with unprecedented accuracy, before patients are placed in harm’s way. Ou...

## Key facts

- **NIH application ID:** 9935103
- **Project number:** 5R01HD092239-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** James L Coyle
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $302,394
- **Award type:** 5
- **Project period:** 2017-08-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9935103

## Citation

> US National Institutes of Health, RePORTER application 9935103, Instrumental screening for dysphagia by combining high-resolution cervical auscultation with advanced data analysis tools to identify silent dysphagia and silent aspiration (5R01HD092239-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9935103. Licensed CC0.

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