Standardisation of an AI-based vocal fold assessment tool on a recurrent respiratory papillomatosis model.

Objective: The assessment of extension of papilloma growth in recurrent respiratory papillomatosis (RRP) on vocal folds can be performed quantitatively utilising artificial intelligence (AI).

Methods: This study evaluated the efficacy of an AI-based annotation system, Glottis Coverage - Artificial Intelligence and Deep learning (GC-AID) in 4 patients to assess affected mucosa in white light (WL) and narrow band imaging modalities as a case-study for future applications.

Results: In healthy larynges, the mean difference between areas of the right and left vocal folds was minimal (2.6%). For patient # 4, following treatment, RRP coverage in WL decreased from 69.5% to 42.6%. A similar improvement was observed for patient # 1, while no significant benefits were noted for patients # 2 and # 3.

Conclusions: The extent of RRP was precisely measured with GC-AID before and after treatment. Obtaining objective, quantitative results was possible with frame extraction and annotation using the system described herein.