Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.

ABSTRACT: This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The Hammersmith Infant Neurological Examination (HINE) was performed in infants at 4 months of corrected age. Joint positional data were extracted using a pretrained pose-estimation model. Complexity and similarity indices of joint angle and angular velocity in terms of sample entropy and Pearson correlation coefficient were compared between the infants with HINE < 60 and ≥ 60. Video images of spontaneous movements were recorded in 65 preterm infants at term-equivalent age. Complexity indices of joint angles and angular velocities differed between

SUBMITTER: Shin HI 

PROVIDER: S-EPMC8873498 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature