Bruschetta Roberta, Caruso Angela, Micai Martina, Campisi Simona, Tartarisco Gennaro, Pioggia Giovanni, Scattoni Maria Luisa
Italian National Research Council, Institute for Biomedical Research and Innovation, Via Leanza, Istituto Marino, 98164 Messina, Italy.
Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
Diagnostics (Basel). 2025 Jan 8;15(2):136. doi: 10.3390/diagnostics15020136.
The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and improved long-term outcomes. Recent evidence indicates a correlation between deficits in spontaneous movements in newborns and the likelihood of developing NDDs later in life. This study aims to address this aspect by employing a marker-less Artificial Intelligence (AI) approach for the automatic assessment of infants' movements from single-camera video recordings. A total of 74 high-risk infants were selected from the Italian Network for Early Detection of Autism Spectrum Disorders (NIDA) database and closely observed at five different time points, ranging from 10 days to 24 weeks of age. Automatic motion tracking was performed using deep learning to capture infants' body landmarks and extract a set of kinematic parameters. Our findings revealed significant differences between infants later diagnosed with NDD and typically developing (TD) infants in three lower limb features at 10 days old: 'Median Velocity', 'Area differing from moving average', and 'Periodicity'. Using a Support Vector Machine (SVM), we achieved an accuracy rate of approximately 85%, a sensitivity of 64%, and a specificity of 100%. We also observed that the disparities in lower limb movements diminished over time points. Furthermore, the tracking accuracy was assessed through a comparative analysis with a validated semi-automatic algorithm (Movidea), obtaining a Pearson correlation (R) of 93.96% (88.61-96.60%) and a root mean square error (RMSE) of 9.52 pixels (7.29-12.37). This research highlights the potential of AI movement analysis for the early detection of NDDs, providing valuable insights into the motor development of infants at risk.
婴儿神经发育障碍（NDDs）的早期识别对于有效干预和改善长期预后至关重要。最近的证据表明，新生儿自发运动缺陷与日后患NDDs的可能性之间存在关联。本研究旨在通过采用无标记人工智能（AI）方法，从单摄像头视频记录中自动评估婴儿运动，来解决这一问题。从意大利自闭症谱系障碍早期检测网络（NIDA）数据库中选取了74名高危婴儿，并在10天至24周龄的五个不同时间点进行密切观察。使用深度学习进行自动运动跟踪，以捕捉婴儿的身体标志点并提取一组运动学参数。我们的研究结果显示，在10日龄时，日后被诊断为NDD的婴儿与正常发育（TD）婴儿在三个下肢特征上存在显著差异：“中位数速度”、“与移动平均值不同的面积”和“周期性”。使用支持向量机（SVM），我们实现了约85%的准确率、64%的灵敏度和100%的特异性。我们还观察到下肢运动的差异随时间点逐渐减小。此外，通过与经过验证的半自动算法（Movidea）进行对比分析来评估跟踪准确性，得到的皮尔逊相关系数（R）为93.96%（88.61 - 96.60%），均方根误差（RMSE）为9.52像素（7.29 - 12.37）。这项研究突出了AI运动分析在NDDs早期检测中的潜力，为有风险的婴儿的运动发育提供了有价值的见解。
