使用iPhone结构光面部扫描开发3D设计个性化新生儿CPAP面罩的工作流程。

IF 3.2 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Amika A Kamath, Marielle J Kamath, Selin Ekici, Anna Sofia Stans, Christopher E Colby, Jane M Matsumoto, Mark E Wylam
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引用次数: 2

摘要

背景:持续气道正压通气(CPAP)是新生儿重症监护病房常用的呼吸支持模式。在早产儿中,鼻腔CPAP (nCPAP)治疗通常通过柔软的、生物相容性的鼻罩进行,适合长期直接接触皮肤,并牢固地贴在脸上。在这个脆弱的人群中,有限尺寸的nCPAP口罩会导致不合适的并发症和不良后果。我们假设定制的nCPAP口罩可以改善新生儿的贴合度,减少皮肤压力和带张力,提高疗效并减少nCPAP治疗相关的并发症。方法:在获得IRB批准和知情同意后,我们评估了几种方法来开发3D面部模型来测试定制的3D nCPAP口罩。这些方法包括基于相机的摄影测量,激光扫描和结构光扫描,使用Bellus3D Face Camera Pro和iPhone X运行Bellus3D FaceApp for iPhone或Heges应用程序。这些数据被用来提供精确的新生儿面部三维模型。利用CAD软件设计nCPAP插入件,将其放置在专有的nCPAP面罩和模型婴儿的面部之间。三维设计的nCPAP口罩与模型面部贴合。随后,将nCPAP口罩连接到呼吸机以提供CPAP,并放置校准的压力传感器和共线性张力传感器以测量皮肤压力和nCPAP口罩带张力。结果:摄影测量和激光扫描不适合新生儿面部。然而,结构光扫描技术产生了精确的新生儿面部3D模型。与没有植入物的CPAP口罩相比,使用3D打印模具和硅注入制造的个性化nCPAP口罩插入物可以有效地降低表面压力和口罩带压力,在某些情况下降低了50%以上。结论:我们发现现成的结构光扫描设备,如iPhone X,是一种低成本、安全、快速和准确的工具,可以建立准确的早产儿面部地形模型。结构光扫描开发了3D nCPAP插入物,应用于市售的CPAP面罩,当用于模型新生儿面部时,可显著降低临床相关CPAP压力下的皮肤压力和带张力。该工作流程可能有助于为新生儿生产个性化的nCPAP口罩,减少因不匹配而引起的并发症。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning.

Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning.

Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning.

Workflow to develop 3D designed personalized neonatal CPAP masks using iPhone structured light facial scanning.

Background: Continuous positive airway pressure (CPAP) is a common mode of respiratory support used in neonatal intensive care units. In preterm infants, nasal CPAP (nCPAP) therapy is often delivered via soft, biocompatible nasal mask suitable for long-term direct skin contact and held firmly against the face. Limited sizes of nCPAP mask contribute to mal-fitting related complications and adverse outcomes in this fragile population. We hypothesized that custom-fit nCPAP masks will improve the fit with less skin pressure and strap tension improving efficacy and reducing complications associated with nCPAP therapy in neonates.

Methods: After IRB approval and informed consent, we evaluated several methods to develop 3D facial models to test custom 3D nCPAP masks. These methods included camera-based photogrammetry, laser scanning and structured light scanning using a Bellus3D Face Camera Pro and iPhone X running either Bellus3D FaceApp for iPhone, or Heges application. This data was used to provide accurate 3D neonatal facial models. Using CAD software nCPAP inserts were designed to be placed between proprietary nCPAP mask and the model infant's face. The resulted 3D designed nCPAP mask was form fitted to the model face. Subsequently, nCPAP masks were connected to a ventilator to provide CPAP and calibrated pressure sensors and co-linear tension sensors were placed to measures skin pressure and nCPAP mask strap tension.

Results: Photogrammetry and laser scanning were not suited to the neonatal face. However, structured light scanning techniques produced accurate 3D neonatal facial models. Individualized nCPAP mask inserts manufactured using 3D printed molds and silicon injection were effective at decreasing surface pressure and mask strap pressure in some cases by more than 50% compared to CPAP masks without inserts.

Conclusions: We found that readily available structured light scanning devices such as the iPhone X are a low cost, safe, rapid, and accurate tool to develop accurate models of preterm infant facial topography. Structured light scanning developed 3D nCPAP inserts applied to commercially available CPAP masks significantly reduced skin pressure and strap tension at clinically relevant CPAP pressures when utilized on model neonatal faces. This workflow maybe useful at producing individualized nCPAP masks for neonates reducing complications due to misfit.

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