Machine Learning for Automatic Detection of Velopharyngeal Dysfunction: Proof of Concept

Background: Even after palatoplasty, the incidence of velopharyngeal dysfunction (VPD) can reach 30%; however, these estimates arise from high-income countries (HICs) where speech-language pathologists are part of standardized cleft teams. The VPD burden in low- and middle-income countries (LMICs) is unknown. This study aims to develop a machine learning model that can detect the presence of VPD using audio samples alone. Methods: Case and control audio samples were obtained by institutional and publicly available sources. A machine learning model was built using Python software. Results: 110 initial audio samples used to test and train the model were re-tested after format conversion and file deidentification. Each sample was tested 5 times yielding a precision of 100%. Sensitivity was 92.73% (95% CI 82.41%-97.98%) and specificity was 98.18% (95% CI 90.28%-99.95%). 113 prospective samples, which had not yet interacted with the model, were then tested. Precision was again 100% with a sensitivity of 88.89% (95% CI 78.44%-95.41%) and a specificity of 66% (95% CI 51.23%-78.79%). Discussion: VPD affects nearly 100% of patients with unrepaired overt soft palatal clefts and nearly 30% of patients who have undergone palatoplasty. VPD can render patients unintelligible, thereby accruing significant psychosocial morbidity. The burden of VPD in LMICs is unknown, and likely exceeds estimates from HICs. The ability to access a phone-based screening machine learning model could expand access to diagnostic, and potentially therapeutic, modalities for an innumerable amount of patients world-wide who suffer from VPD.