SBIR Phase II: Monitoring and management of infant cranial development at the point-of-care

这一小企业创新研究（SBIR）第二阶段项目的更广泛影响/商业潜力包括在护理点（即儿科医生办公室和家庭）支持新生儿和婴儿保健服务。该项目将开发定量想象和机器学习技术，使儿科医生能够在健康儿童访问和/或远程访问期间评估和监测婴儿的头部发育。如果一个孩子被诊断为头部畸形，早期和有效的治疗可以开始。因此，这个项目的一个主要影响是大大减少了未得到治疗并面临潜在健康风险的颅骨畸形儿童的人数。改善这些疾病的临床管理可能会降低相关的医疗保健成本并改善患者的预后。我们的技术将被打包成智能手机或平板电脑应用程序，使资源匮乏的社区和无法进入专业诊所的患者能够获得服务，并在社交距离和临床服务减少的时期提供护理。该项目最终将提高人们对头部畸形风险的认识，改善儿童的健康状况。这个小企业创新研究（SBIR）第二阶段项目将开发定量成像和机器学习算法，利用智能手机拍摄的婴儿头部照片精确测量颅骨形状和大小。这些参数通常用于监测儿童发育，并在诊断和治疗计划与头部变形的常见情况。在这个项目中，我们将使用神经网络完全自动化测量，并通过考虑内生变量（如性别和年龄）的回归模型最大限度地提高准确性。即使在新手用户操作过程中，为了生成准确的头部形状测量，我们基于机器学习的新算法将自动选择更好的图像来检测和分类颅骨变形的类型。通过解决与新手使用工具相关的错误，例如纠正相机使用中的变化，该技术将确保具有不同技术技能的操作人员的合规性和可访问性。此外，我们将利用现代智能设备技术推进三维颅骨形状的重建。这种方法可以处理物体运动，并可以直接在智能手机/平板电脑上实现，以计算完整的三维形状分析。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project includes the support of newborn and infant health services at the point-of-care (i.e., in pediatrician offices and at home). This project will develop quantitative imagining and machine learning technology that will allow pediatricians to evaluate and monitor the infant’s head development during well-child visits and/or remotely. If a child is diagnosed with head deformations, early and effective therapy can be initiated. Thus, a major impact of this project is significant reduction of the number of children with cranial deformations who remain untreated and are exposed to potential health risks. Improved clinical management of these conditions will potentially lower the associated healthcare costs and improve patient outcome. Our technology will be packaged as a smartphone or tablet app, enabling access to lower-resourced communities and patients without access to specialized clinics, as well as enabling care in periods of social distancing and reduced clinical services. This project will ultimately increase awareness of the risks of head deformations and improve pediatric health outcomes.This Small Business Innovation Research (SBIR) Phase II project will develop quantitative imaging and machine learning algorithms that accurately measure the cranial shape and size using photos of the infant head taken with a smartphone. These parameters are commonly used to monitor child development, and in the diagnosis and treatment planning of common conditions with head deformations. In this project, we will fully automate the measurements using neural networks and maximize accuracy with regression models accounting for endogenous variables, such as sex and age. To generate accurate head shape measurements even during operation by novice users, our new algorithms based on machine learning will automatically select better images to detect and classify the type of cranial deformation. By addressing errors related to novice use of the tool, such as correcting for variations in the use of the camera, the technology will ensure compliance and accessibility to operators with variable technological skills. Furthermore, we will advance reconstruction of the three-dimensional cranial shape utilizing modern smart device technologies. This approach can handle object motion and can be implemented directly on a smartphone/tablet to compute full three-dimensional shape analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.