# An AI assistance tool to guide novice practitioners in the competent performance of flexible video laryngoscopy

> **NIH NIH R41** · PERCEPTRON HEALTH, INC. · 2022 · $275,713

## Abstract

Abstract
Perceptron Health proposes to develop an artificial intelligence (AI) software and image processing
assistance tool that trains advanced practice providers (APPs) to perform a competent flexible fiberoptic
laryngoscopy (FFL) on patients and improve uptake of their skills. With 65.7% of U.S. counties lacking a
practicing ear, nose, and throat physician (ENT), this has led to a disparity in care based on geographical
location. Those in rural areas are most impacted. The novel AI tool has the potential to increase the pool of
clinicians capable of performing laryngoscopy from 13,000 ENTs to almost 500,000 APPs, filling a critical care
gap. The toolkit will guide APP users through the laryngoscopy procedure to ensure all anatomical structures
and patient tasks are sufficiently captured. A recording can then be reviewed and interpreted remotely by an
ENT physician, allowing them to focus on diagnosis and treatment. The AI-based product will include an image
capture guidance system as well as a procedure checklist and quality check system that tracks successful
capture of diagnosable views of key anatomical structures.
Perceptron Health plans to assess technical feasibility of the toolkit through the following Phase I Objectives: 1.
Develop a prototype software toolkit that provides guidance through the laryngoscopy procedure; 2. Test the
prototype tool’s capability to improve the ability to perform laryngoscopy on manikins; and 3. Assess the AI’s
ability to generalize to human anatomy in pre-recorded video.
Perceptron’s tool will expand patient access to FFLs via the creation of a practitioner assistance tool able to
identify anatomical structures, localize the camera relative to anatomical structures, and provide guidance to the
user through a user interface (UI). The prototype to be generated in this project will require the novel development
of algorithms capable of classifying images from laryngoscopy videos through the development of state-of-the-
art convolutional neural networks that will allow for the integration of AI algorithms into laryngoscopes. The
proposed algorithms can provide substantial improvements relative to conventional approaches and will have
application in numerous other medical endoscopy contexts (gastrointestinal, pulmonary, and others) in addition
to processing images from laryngoscopy videos. Once fully developed, this innovation will allow non-ENT
clinicians to expand their scope of practice while supporting the ability of both ENTs and speech language
pathologists to perform more remote care and reach more patients. Other potential users of the technology
include ER physicians and anesthesiologists. Importantly, the proposed technology is expected to improve health
by expanding socioeconomic access to specialty care and decreasing time to treatment.

## Key facts

- **NIH application ID:** 10602717
- **Project number:** 1R41EB034186-01
- **Recipient organization:** PERCEPTRON HEALTH, INC.
- **Principal Investigator:** Nasir Islam Bhatti
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $275,713
- **Award type:** 1
- **Project period:** 2022-09-16 → 2024-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10602717

## Citation

> US National Institutes of Health, RePORTER application 10602717, An AI assistance tool to guide novice practitioners in the competent performance of flexible video laryngoscopy (1R41EB034186-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10602717. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*