Evaluating the feasibility of an innovative point-of-care screening tool for detection of infant motor delay within the newborn period

许多慢性神经发育疾病的发病，如脑瘫、神经肌肉 疾病和其他神经遗传疾病发生在生命的围产期阶段。检测到这些 在婴儿期，当大脑的可塑性达到最高时，情况有可能显著改善 长期结果。在美国(美国)脑性瘫痪等疾病的诊断年龄 对于已经知道围产期危险因素的儿童来说，年龄在2岁左右。多达一半的 然而，残疾儿童的怀孕并不复杂，其诊断通常是 甚至在生命的后期也会发生。令人担忧的是，对来自服务不足社区的儿童的诊断可能是 晚到5岁。诊断这些病症的传统方法是“观望”。 直到孩子最终错过运动里程碑，这样婴儿就错过了关键的几个月。 可能的早期干预。对于有围产期危险因素的婴儿，有时会得到诊断。 通过使用昂贵的技术，如磁共振成像；或专门评估 技术，如一般运动评估(GMA)。然而，这些测试通常只是 在三级护理中心提供。因此，一个护理点新生儿筛查系统可以改变 诊断过程，使每个儿童都有机会及早发现异常运动 新生儿时期的模式。我们的创新系统BabySure使用了人工智能 从一个简单的、非侵入性的视频记录中评估婴儿运动特征的模型。我们的飞行员 数据显示，BabySure可以评估自发运动并将其归类为健康或 在6个月前的婴儿中出现异常。为了确保每个婴儿都有机会获得 为了早期、准确地诊断，我们将在社区新生儿托儿所试点新生儿筛查计划。 BabySure使用自动骨骼跟踪来提取33个骨骼地标的位置坐标 从婴儿自发运动的每个视频帧(包括四肢、头部、 脸和躯干)。我们项目的一个重要部分是继续努力在 肤色的多样性一直以来，计算机视觉对较黑的皮肤都不够准确。至 训练我们的模型来识别异常运动，我们将使用黄金标准GMA来对婴儿进行分类 异常或健康的运动训练组。我们的分析计划将构建三个人工智能的原型 集成单个分类器的基本模型(每个模型有两个头：回归和分类) 包括为图像处理和定制训练而预先训练的微调卷积神经网络 模特们。最终的模型将输出运动功能评分，将婴儿的动作归类为典型动作 或者是反常。此外，我们将评估结果的日内和日间变异性，以通知未来 建议在整个新生儿期进行运动筛查的最佳时机或顺序。

The onset of many chronic neurodevelopmental conditions, such as cerebral palsy, neuromuscular conditions, and other neurogenetic disorders occur within the perinatal phase of life. Detection of these conditions, during infancy, when brain plasticity is at its highest, has the potential to dramatically improve long term outcomes. In the United States (U.S.) the age of diagnosis of conditions such as cerebral palsy is around 2 years of age for children who had known perinatal risk factors. As many as half of the children with a disability, however, are from an uncomplicated pregnancy and the diagnosis typically occurs even later in life. Alarmingly, a diagnosis for a child from an underserved community can be as late as 5 years of age. The traditional paradigm for diagnosis of these conditions, uses a ‘wait-and-see’ approach until the child eventually misses motor milestones, thus the infant has missed critical months of possible early intervention. For infants with perinatal risk factors, a diagnosis is sometime achieved through the use of costly technologies, such as magnetic resonance imaging; or specialized evaluation techniques, such as the General Movements Assessment (GMA). However, these tests are typically only available in tertiary care centers. Thus, a point of care newborn screening system could transform the diagnostic process, allowing every child the opportunity for early detection of aberrant movement patterns within the newborn period. Our innovative system, BabySure uses an artificial intelligence model to evaluate infant movement characteristics from a simple, non-invasive video recording. Our pilot data has shown that BabySure can evaluate and categorize spontaneous movements as healthy or aberrant in infants before the age of 6 months. To ensure every baby has the opportunity to receive an early, accurate diagnosis, we will pilot a newborn screening program in a community newborn nursery. BabySure uses automated skeletal tracking to extract positional coordinates of 33 skeletal landmarks from each video frame of the infant’s spontaneous movements (including points on the extremities, head, face, and trunk). An essential part of our project is to continue our efforts to validate our system on a variety of skin tones as historically, computer vision has not been sufficiently accurate on darker skin. To train our model to identify aberrant movement we will use the gold-standard GMA to classify infants in aberrant or healthy movement training groups. Our analysis plan will prototype three artificial intelligence base models to ensemble a single classifier (each with two heads: regression and classification) including fine tuning convolutional neural networks pre-trained for image processing and custom-training models. The final model will output a motor function score that will classify the infant movement as typical or aberrant. In addition, we will evaluate the intra- and inter-day variability of results to inform future recommendations for optimal timing or sequence of movement screening across the newborn period.