Mobile Three-Dimensional Screening for Cranial Malformations

颅骨畸形移动三维筛查

• 批准号: 10888913
• 负责人: Fereshteh Aalamifar
• 金额: $ 4.46万
• 依托单位: PEDIAMETRIX INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10888913
• 关键词: 3-Dimensional; Address; Affect; Algorithms; Attention; Cellular Phone; Cephalic; Child; Childhood; Classification; Clinical; Complex; Craniosynostosis; Data; Data Analyses; Data Collection; Diagnosis; Early Diagnosis; Early treatment; Epidemic; Evaluation; Feasibility Studies; Growth; Head; Head circumference; Health; Health Care Costs; Health Personnel; Infant; Institution; Left; Location; Machine Learning; Measurement; Measures; Methods; Mission; Monitor; Morbidity - disease rate; Pediatric Hospitals; Phase; Prevalence; Scanning; Shapes; Small Business Innovation Research Grant; Technology; Time; Training; Visit; Well Child Visits; cost; cranium; deep learning model; design; digital health; digital tool; effective therapy; improved; infancy; machine learning method; malformation; medical complication; new technology; pediatrician; plagiocephaly; point of care; prevent; quantitative imaging; reconstruction; research clinical testing; screening; social anxiety; societal costs; support tools; tool

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.

抽象的 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影响 成本。早期检测的一个关键挑战是儿科办公室缺乏可用的工具来执行 在健康儿童就诊期间进行定量头部形状评估。诊断延迟限制了早期诊断的机会， 侵入性较小且有效的治疗选择。在这个快速 SBIR 项目中，PediaMetrix Inc. 联手 与儿科医院和提供商合作开发和评估 SoftSpotTM，这是第一个移动数字工具 婴儿颅骨畸形的护理点 3D 数据收集和分析。 婴儿期的头部畸形可以是缝缝早闭（即颅缝早闭）或非缝缝早闭（例如 变形性斜头畸形和短头畸形或 DPB）。这两种情况都需要立即引起注意 并受益于早期治疗，以避免长期的健康并发症。 DPB 患病率增加 近年来，这种情况急剧增加，从 5% 增加到大约 20%-30%，导致这种情况被称为儿科疾病 流行性。颅缝早闭不太常见，每 2,000 名儿童中就有 1 人患有颅缝早闭。加强早期管理 这些情况并为了防止更复杂的治疗和相关的发病率，有必要监测 婴儿头部在护理点的生长情况。 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数字工具，该工具将能够 儿科医生捕获并分析每个婴儿的 3D 扫描，以便早期诊断和管理颅骨疾病 畸形。在该项目的第一阶段，我们将开发一种新技术来快速捕获和分析 只需几秒钟即可获得颅骨顶部的 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，包括每个孩子定期进行的头围测量 访问过，但目前使用的是过时且不可靠的卷尺。我们的技术将专为 在儿童健康检查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 通过智能手机重建和分析整个颅骨。我们还将训练深度学习模型 对颅缝早闭和其他颅部疾病的类型进行分类，并进行临床评估和用户可行性 研究。 PediaMetrix 的总体使命是为儿科健康提供准确的决策支持工具 护理点。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包装为移动数字健康解决方案 儿科医疗服务提供者可以随时随地访问。 SoftSpot3DTM的成功演示将 导致患有未经治疗的颅部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。