Groos Daniel, Adde Lars, Støen Ragnhild, Ramampiaro Heri, Ihlen Espen A F
Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.
Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
Comput Med Imaging Graph. 2022 Jan;95:102012. doi: 10.1016/j.compmedimag.2021.102012. Epub 2021 Nov 26.
Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance.
对自发运动的评估可以预测高危婴儿的长期发育障碍。为了开发用于自动预测后期障碍的算法，需要通过婴儿姿势估计对身体部位和关节进行高精度定位。在一个新颖的婴儿姿势数据集上训练并评估了四种类型的卷积神经网络，该数据集涵盖了来自一个国际临床社区的1424个视频中的巨大变化。网络的定位性能通过估计的关键点位置与人类专家注释之间的偏差来评估。还评估了计算效率，以确定神经网络在临床实践中的可行性。表现最佳的神经网络具有与人类专家注释的评分者间差异相似的定位误差，同时仍能高效运行。总体而言，我们的研究结果表明，婴儿自发运动的姿势估计具有巨大潜力，可通过以人类水平的性能对视频记录中的婴儿运动进行量化，来支持有关围产期脑损伤儿童发育障碍早期检测的研究项目。
