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

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.Narrative Perceptron’s software and imaging assistance tool will ultimately result in a commercial product that has potential for high impact by expanding the number and type of medical practitioners who can perform competent flexible fiberoptic laryngoscopy on patients. This is expected to result in improved health of the US population by reducing disparities in access to care, catching disease progression earlier, and decreasing disease burden and costs. Additional benefits include the ability to replace subjective estimations with objective measurements, and to generate data for a research database of conditions.