Automated Detection of Palatal Deformations Using Deep Learning on Endoscopic Images

Abstract

A deformation of the hard palate can occur in spinal muscular atrophy and leads to problems with feeding and swallowing in early childhood. An objective analysis of the palatal changes is therefore desirable for early treatment initiation. In this study, we investigate a deep learning approach to automatically detect deformation in endoscopic images which were collected in a prospective in-vivo study on 33 infants. Ratings of five different experts were used to quantify the deformation and to train our network. We investigate different network architectures and data set splits and achieve classification performances of up to 0.85 ± 0.05 when distinguishing between normal and deformation using the EfficientNet architecture. This combination of endoscopic imaging and deep learning offers a first approach for the objective assessment of palatal changes.