Development of Machine Learning Copilot to Assist Novices in Learning Flexible Laryngoscopy

INTRODUCTION

It has been well documented that most otolaryngologists practice within densely populated metropolitan areas. Two-thirds of U.S. counties lack a practicing otolaryngology-head and neck surgery (OHNS) specialist, with their density per county being associated with the highest education and income quartiles. Using the Centers for Medicare and Medicaid Services (CMS) Provider Utilization and Payment Data Physician and Other Supplier PUF dataset, Davis et al. (J Voice. doi.org/10.1016/j. jvoice.2021.05.002) observed a direct association between access to otolaryngologists able to perform flexible fiberoptic laryngoscopy (FFL) based on billing codes and number of Medicare enrollees, further demonstrating the disproportionate clustering of otolaryngologists in urban areas, consistent with prior studies. The lack of access to OHNS among low-income rural areas further exacerbates healthcare disparities across the U.S. Telemedicine has been a solution proposed to alleviate some of the socioeconomic inequity, but has several limitations within OHNS, namely being unable to remotely perform FFL as part of a physical exam.

FFL is one of the most common procedures performed by otolaryngologists and is an early core competency that OHNS residents are expected to acquire. It allows for detailed examination of the sinonasal cavity, nasopharynx pharynx, and larynx, and is crucial in the evaluation of patients with voice, swallowing, and airway symptoms, patients needing airway management such as awake fiberoptic nasotracheal intubation, and those with head and neck cancer. Several steps comprise a successful FFL, including identification of key structures, successful navigation of the patient’s anatomy, patient comfort/coaching, and visual capture of each anatomical area of interest, which have varying difficulty depending on patient anatomy, pathology, and learner experience. Recently, interest has grown within other specialties to train residents and advanced practice providers (APPs) to perform FFL. For instance, Price et al. (Int J Radiat Oncol Biol Phys. doi.org/10.1016/j.ijrobp.2020.05.009) demonstrated that, within radiation oncology, a simulation-based training (SBT) workshop on FFL for radiation oncology residents was both feasible and increased confidence in head and neck anatomy and FFL procedural skills.

Previous work has shown that a learning curve exists for novices learning to become competent in performing FFL using a manikin model, with a mean of six attempts required to achieve competency based on a validated checklist. This learning curve has historically been addressed by having residents perform the procedure on patients needing evaluation; however, this approach risks discomfort, nasal bleeding, and mucosal injury to the patient. These risks are greatest before competence is achieved by the learner. SBT has been proposed as a tool for novices to achieve competence in a variety of otolaryngologic procedures, and both trainers and trainees have ranked FFL as a skill where SBT would be highly useful.