Mean 3D Dispersion for Automatic General Movement Assessment of Preterm Infants

Ameur Soualmi, Olivier Alata, Christophe Ducottet, Hugues Patural, Antoine Giraud

Abstract: The General Movement assessment (GMA) is a validated assessment of brain maturation primarily based on the qualitative analysis of the complexity and the variation of spontaneous motor activity. The GMA can identify preterm infants presenting an early abnormal developmental trajectory before term-equivalent age, which permits a personalized early developmental intervention. However, GMA is time-consuming and relies on a qualitative analysis; these limitations restrict the implementation of GMA in clinical practice. In this study based on a validated dataset of 183 videos from 92 premature infants (54 males, 38 females) born <33 weeks of gestational age (GA) and acquired between 32 and 40 weeks of GA, we introduce the mean 3D dispersion (M3D) for objective quantification and classification of normal and abnormal GMA. Moreover, we have created a new 3D representation of skeleton joints which allows an objective comparison of spontaneous movements of infants of different ages and sizes. Preterm infants with normal versus abnormal GMA had a distinct M3D distribution (p <0.001). The M3D has shown a good classification performance for GMA (AUC=0.7723) and presented an accuracy of 74.1%, a sensitivity of 75.8%, and a specificity of 70.1% when using an M3D of 0.29 as a classification threshold.Clinical relevance— Our study paves the way for the development of quantitative analysis of GMA within the Neonatal Unit.