We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.
- Authors
Shin, Hyun Iee; Shin, Hyung-Ik; Bang, Moon Suk; Kim, Don-Kyu; Shin, Seung Han; Kim, Ee-Kyung; Kim, Yoo-Jin; Lee, Eun Sun; Park, Seul Gi; Ji, Hye Min; Lee, Woo Hyung
- 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 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.
- Subjects
PREMATURE infants; DEEP learning; INFANT development; ANGULAR velocity; QUANTITATIVE research; PEARSON correlation (Statistics); MACHINE learning
- Publication
Scientific Reports, 2022, Vol 12, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-022-07139-x