Identification of smile events using automated facial expression recognition during the Autism Diagnostic Observation Schedule (ADOS-2): a proof-of-principle study

Introduction: The diagnosis of autism spectrum disorder (ASD) is resource-intensive and associated with long waiting times. Digital screenings using facial expression recognition (FER) are a promising approach to accelerate the diagnostic process while increasing its sensitivity and specificity. The aim of this study is to examine whether the identification of smile events using FER in an autism diagnosis utilisation population is reliable.
Methods: From video recordings of children undergoing the Autism Diagnostic Observation Schedule (ADOS-2) due to suspected ASD, sequences showing smile and non-smile events were identified. It is being investigated whether the FER reliably recognizes smile events and corresponds to a human rating.
Results: The FER based on the facial action unit mouthSmile accurately identifies smile events with a sensitivity of 96.43% and a specificity of 96.08%. A very high agreement with human raters (κ = 0.918) was achieved.
Discussion: This study demonstrates that smile events can in principle be identified using FER in a clinical utilisation population of children with suspected autism. Further studies are required to generalise the results.