Mobile Three-Dimensional Screening for Cranial Malformations

摘要 婴儿头部畸形的延迟识别正在导致不必要的医疗并发症和社会问题。 成本早期发现的一个关键挑战是儿科办公室缺乏工具来执行 在健康儿童访问期间进行定量头型评估。诊断的延迟限制了早期， 更少的侵入性和有效的治疗选择。在这个快速通道SBIR项目中，PediaMeetings Inc.联手 与儿科医院和供应商一起开发和评估SoftSpotTM，这是第一个移动的数字工具， 在护理点对婴儿颅骨畸形进行3D数据收集和分析。 婴儿期的头部畸形可能是骨性结合（即，颅缝早闭）或非骨性早闭（如 变形斜头畸形和短头畸形或DPB）。这两种情况都需要立即注意 并从早期治疗中受益，以避免长期的健康并发症。DPB患病率增加 近年来急剧增加，从5%增加到约20%-30%，导致这种情况被称为儿科 疫情颅缝早闭不太常见，每2,000名儿童中有1名。改善早期管理， 这些条件，并防止更复杂的治疗和相关的发病率，这是必不可少的监测 婴儿头部在护理点的生长。 为了解决这一未满足的临床需求，我们将开发和评估一种移动的数字工具， 儿科医生捕捉和分析每个婴儿的3D扫描，以早期诊断和管理颅 畸形在这个项目的第一阶段，我们将开发一种新的技术， 几秒钟内就能获得颅骨顶部的3D数据。我们将使用机器学习方法来自动计算 头部形状参数，包括头围，这是在每个孩子定期进行 访问，但目前与一个过时的和不可靠的卷尺。我们的技术将被设计用于 在健康儿童访视期间对所有婴儿进行一般颅骨评估。在第二阶段，我们将开发3D方法， 通过智能手机对整个颅骨进行重建和分析我们还将训练深度学习模型， 对颅缝早闭和其他颅骨疾病进行分类，并进行临床评价和用户可行性 问题研究 PediaMeetings的总体使命是为儿科健康提供准确的决策支持工具， 即时护理这将通过机器学习和定量成像算法来实现， 与智能手机技术进步相结合，将被打包为移动的数字健康解决方案 儿科保健提供者可随时随地访问。SoftSpot 3DTM的成功演示将 导致未经治疗的颅骨疾病的儿童数量显着减少， 降低相关的医疗费用和社交焦虑。

ABSTRACT Delayed identification of infant head malformation is causing unnecessary medical complications and societal costs. A critical challenge in the early detection is the absence of tools available to pediatric offices to perform quantitative head shape assessment during well-child visits. Delays in diagnosis limit the opportunity for early, less invasive and effective treatment options. In this Fast-track SBIR project, PediaMetrix Inc. has joined forces with pediatric hospitals and providers to develop and evaluate SoftSpotTM, which is the first mobile digital tool for 3D data collection and analysis of infant cranial malformations at the point-of-care. Head malformations during infancy can be synostotic (i.e., craniosynostosis) or nonsynostotic (such as deformational plagiocephaly and brachycephaly or DPB). Both types of conditions require immediate attention and benefit from early treatment to avoid long-term health complications. The prevalence of DPB increased dramatically in recent years, from 5% to approximately 20%-30%, causing the condition to be called a pediatric epidemic. Craniosynostosis is less common affecting 1 in 2,000 children. To improve the early management of these conditions and to prevent more complex treatment and associated morbidities, it is essential to monitor the growth of the infant head at the point-of-care. To address this unmet clinical need, we will develop and evaluate a mobile digital tool that will enable pediatricians to capture and analyze 3D scans of every infant for the early diagnosis and management of cranial malformations. In the Phase I of this project, we will develop a novel technology to rapidly capture and analyze 3D data of the top of cranium in just seconds. We will use machine learning methods to automatically compute the head shape parameters, including the head circumference which is routinely performed during every child visit, but currently with an outdated and unreliable measuring tape. Our technology will be designed for the general cranial evaluation of all infants during well-child visits. In Phase II, we will develop methods for the 3D reconstruction and analysis of the full cranium from a smartphone. We will also train deep learning models to classify types of craniosynostosis and other cranial conditions and conduct clinical evaluation and user-feasibility studies. The overall mission of PediaMetrix is to provide accurate decision support tools for pediatric health at the point-of-care. This will be achieved through machine learning and quantitative imaging algorithms that in combination with smartphone technological advances will be packaged as mobile digital health solutions accessible to pediatric health providers at any time and location. Successful demonstration of SoftSpot3DTM will lead to a significant reduction of the number of children left with untreated cranial conditions in addition to lowering the associated healthcare costs and social anxiety.