Using Artificial Intelligence for Assessment of Velopharyngeal Competence in Children Born With Cleft Palate With or Without Cleft Lip.

Objective: Development of an AI tool to assess velopharyngeal competence (VPC) in children with cleft palate, with/without cleft lip.

Design: Innovation of an AI tool using retrospective audio recordings and assessments of VPC.

Setting: Two datasets were used. The first, named the SR dataset, included data from follow-up visits to Skåne University Hospital, Sweden. The second, named the SC + IC dataset, was a combined dataset (SC + IC dataset) with data from the Scandcleft randomized trials across five countries and an intercenter study performed at six Swedish CL/P centers.

Participants: SR dataset included 153 recordings from 162 children, and SC + IC dataset included 308 recordings from 399 children. All recordings were from ages 5 or 10, with corresponding VPC assessments.

Interventions: Development of two networks, a convolutional neural network (CNN) and a pre-trained CNN (VGGish). After initial testing using the SR dataset, the networks were re-tested using the SC + IC dataset and modified to improve performance.

Main outcome measures: Accuracy of the networks' VPC scores, with speech and language pathologistś scores seen as the true values. A three-point scale was used for VPC assessments.

Results: VGGish outperformed CNN, achieving 57.1% accuracy compared to 39.8%. Minor adjustments in data pre-processing and network characteristics improved accuracies.

Conclusions: Network accuracies were too low for the networks to be useful alternatives for VPC assessment in clinical practice. Suggestions for future research with regards to study design and dataset optimization were discussed.