Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
Comput Methods Programs Biomed. 2023 May;233:107455. doi: 10.1016/j.cmpb.2023.107455. Epub 2023 Mar 5.
Neurodevelopmental assessment enables the identification of infant developmental disorders in the first months of life. Thus, the appropriate therapy can be initiated promptly, increasing the chances for correct motor function. Posture asymmetry is one of the crucial aspects evaluated during the diagnosis. Available diagnostic methods are mainly based on qualitative assessment and subjective expert opinion. Current trends in computer-aided diagnosis focus mostly on analyzing infants' spontaneous movement videos using artificial intelligence methods, based primarily on limbs movement. This study aims to develop an automatic method for determining the infant's positional asymmetry in a video recording using computer image processing methods.
We made the first attempt to determine positional preferences in a recording automatically. We proposed six quantitative features describing trunk and head position based on pose estimation. As a result of our algorithm, we estimate the percentage of each trunk position in a recording using known machine learning methods. The training and test sets were created from 51 recordings collected during our research and 12 recordings from the benchmark dataset evaluated by five of our experts. The method was assessed using the leave-one-subject-out cross-validation method for ground truth video fragments and different classifiers. Log loss for multiclass classification and ROC AUC were determined to evaluate the results for both our and benchmark datasets.
In a classification of the shortened side, the QDA classifier yields the most accurate results, gaining the lowest log loss of 0.552 and AUC of 0.913. The high accuracy (92.03) and sensitivity (93.26) confirm the method's potential in screening for asymmetry.
The method allows obtaining quantitative information about positional preference, a valuable extension of basic diagnostics without additional tools and procedures. In combination with an analysis of limbs movement, it may constitute one of the elements of a novelty computer-aided infants' diagnosis system in the future.
神经发育评估可在婴儿生命的头几个月识别其发育障碍。因此，可以及时启动适当的治疗，从而增加运动功能正确恢复的机会。姿势不对称是诊断过程中评估的关键方面之一。现有的诊断方法主要基于定性评估和主观专家意见。当前计算机辅助诊断的趋势主要集中在使用人工智能方法分析婴儿的自发性运动视频上，这些方法主要基于肢体运动。本研究旨在开发一种使用计算机图像处理方法自动确定视频记录中婴儿位置不对称的方法。
我们首次尝试自动确定记录中的位置偏好。我们基于姿势估计提出了六个描述躯干和头部位置的定量特征。通过我们的算法，我们使用已知的机器学习方法估计记录中每种躯干位置的百分比。训练集和测试集是由我们的研究中收集的 51 个记录和由我们的五位专家评估的基准数据集的 12 个记录创建的。该方法使用基于真实视频片段的留一受试者外交叉验证方法和不同的分类器进行评估。为了评估我们的数据集和基准数据集的结果，确定了多类分类的对数损失和 ROC AUC。
在缩短侧的分类中，QDA 分类器产生最准确的结果，对数损失最低为 0.552，AUC 为 0.913。高准确率（92.03%）和高灵敏度（93.26%）证实了该方法在筛查不对称方面的潜力。
该方法允许获得关于位置偏好的定量信息，这是基本诊断的有价值扩展，无需额外的工具和程序。与肢体运动分析相结合，它可能构成未来新颖的计算机辅助婴儿诊断系统的要素之一。
