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

Abstract

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.

Keywords: NBI; artificial intelligence; deep learning; larynx; papillomatosis.

Cover figure.

Endoluminal annotation of the laryngeal pathology in a frame extracted from videos obtained during white light laryngoscopy, showing the intersection of the vocal fold area of interest (green) and the recurrent respiratory papillomatosis area of interest (blue).

Figure 1.

Endoluminal annotation of a vocal fold in narrow band imaging modality of a frame extracted from videos obtained during laryngoscopy. The outline of annotation for the right vocal fold is shown.

Figure 2.

Enlisting the tasks (frames) on the example of one patient, for right vocal fold.

Figure 3.

Diagram of the algorithm that calculates the degree of change of the papilloma coverage area located on the vocal folds.