Automated Quantification of Vocal Fold Motion in a Recurrent Laryngeal Nerve Injury Mouse Model.

Objectives/hypothesis: The goal of this study was to objectively examine vocal fold (VF) motion dynamics after iatrogenic recurrent laryngeal nerve (RLN) injury in a mouse surgical model. Furthermore, we sought to identify a method of inducing injury with a consistent recovery pattern from which we can begin to evaluate spontaneous recovery and test therapeutic interventions.

Study Design: Animal model.

Methods: The right RLN in C57BL/6J mice was crushed for 30 seconds using an aneurysm clip with 1.3-N closing force. Transoral laryngoscopy enabled visualization of VF movement prior to surgery, immediately post-crush, and at two endpoints: 3 days (n = 5) and 2 weeks (n = 5). VF motion was quantified with our custom motion-analysis software. At each endpoint, RLN samples were collected for transmission electron microscopy for correlation with VF motion dynamics.

Results: Our VF tracking software permitted automated quantification of several measures of VF dynamics, such as range and frequency of motion. By 2 weeks post-injury, the frequency of VF movement on the right (injured) side equaled the left, yet range of motion only partially recovered. These objective outcome measures enabled detection of VF dysfunction that persisted at 2 weeks post-crush. Transmission electron microscopy images revealed RLN degeneration 3 days post-crush and partial regeneration at 2 weeks, consistent with functional results obtained with automated VF tracking.

Conclusions: Our motion-analysis software provides novel objective, quantitative, and repeatable metrics to detect and describe subtle VF dysfunction in mice that corresponds with underlying RLN degeneration and recovery. Adaptation of our tracking software for use with human patients is underway.

Level Of Evidence: NA Laryngoscope, 129:E247-E254, 2019.