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

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 the infants with HINE < 60 and >= 60 and correlated positively with HINE scores in most of the joints at the upper and lower extremities (p < 0.05). Similarity indices between each joint angle or joint angular velocity did not differ between the two groups in most of the joints at the upper and lower extremities. Quantitative assessments of spontaneous movements in preterm infants are feasible using a deep learning algorithm and sample entropy. The results indicated that complexity indices of joint movements at both the upper and lower extremities can be potential candidates for detecting developmental outcomes in preterm infants.

Automated summary

This study aimed to develop a quantitative assessment method for spontaneous movements in preterm infants using a deep learning algorithm. The complexity and similarity indices of joint angles and angular velocities were compared between infants with different Hammersmith Infant Neurological Examination (HINE) scores. The results showed that complexity indices of joint movements were positively correlated with HINE scores, suggesting their potential as indicators of developmental outcomes in preterm infants.