Using facial reaction analysis and machine learning to objectively assess the taste of medicines in children.

For orally administered drugs, palatability is key in ensuring patient acceptability and treatment compliance. Therefore, understanding children's taste sensitivity and preferences can support formulators in making paediatric medicines more acceptable. Presently, we explore if the application of computer-vision techniques to videos of children's reaction to gustatory taste strips can provide an objective assessment of palatability. Children aged 4 to 11 years old tasted four different flavoured strips: no taste, bitter, sweet, and sour. Data was collected at home, under the supervision of a guardian, with responses recorded using the Aparito Atom app and smartphone camera. Participants scored each strip on a 5-point hedonic scale. Facial landmarks were identified in the videos, and quantitative measures, such as changes around the eyes, nose, and mouth, were extracted to train models to classify strip taste and score. We received 197 videos and 256 self-reported scores from 64 participants. The hedonic scale elicited expected results: children like sweetness, dislike bitterness and have varying opinions for sourness. The findings revealed the complexity and variability of facial reactions and highlighted specific measures, such as eyebrow and mouth corner elevations, as significant indicators of palatability. This study capturing children's objective reactions to taste sensations holds promise in identifying palatable drug formulations and assessing patient acceptability of paediatric medicines. Moreover, collecting data in the home setting allows for natural behaviour, with minimal burden for participants.