Three-Dimensional-Printed Liver Model Helps Learners Identify Hepatic Subsegments: A Randomized-Controlled Cross-Over Trial.

INTRODUCTION: The purpose of this study was to find out whether 3-dimensional (3D)-printed models improved the learners' ability to identify liver segments. METHODS: A total of 116 physicians from 3 disciplines were tested in a cross-over trial at baseline and after teaching with 3D models and 2-dimensional (2D) images. Adjusted multilevel-mixed models were used to compare scores at baseline and after 3D and 2D. RESULTS: Accuracy in identifying hepatic segments was higher with 3D first than 2D (77% vs 69%; P = 0.05) and not significantly improved by a combination of 3D and 2D. Increased confidence in segment identification was highest in trainees after 3D (P = 0.04). DISCUSSION: 3D-printed models facilitate learning hepatic segmental anatomy.

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.