Accuracy Assessment of EM3D App-Based 3D Facial Scanning Compared to Cone Beam Computed Tomography

Abstract

Background: The use of 3D facial scans is becoming essential for dental practice. However, traditional scanners require labor-intensive procedures and are expensive, making them less accessible in routine clinical practice. In this context, high-performance smartphones and dedicated apps offer a more accessible alternative. This study aims to validate the accuracy of the EM3D app, which utilizes the iPhone’s TrueDepth camera technology, by comparing it to Cone Beam Computed Tomography (CBCT). Methods: Thirty patients requiring CBCT scans were recruited for the study. Facial scans obtained with the TrueDepth camera of the iPhone 13 Pro in conjunction with EM3D app were automatically superimposed onto the 3D models derived from the CBCTs through the implementation of a deep learning methodology. The approach enabled the automatic identification of fifteen landmarks to perform linear and angular measurements for quantitative assessment. A color map was created to highlight discrepancies between the overlaid meshes, and the overall surface differences between the models were automatically quantified. Results: The overall surface difference between the CBCT and EM3D scans was highly accurate, with a mean discrepancy of 0.387 ± 0.361 mm. The mean discrepancies of most measurements were lower than 1 mm (five out of six; 83.33%) between the groups, with no significant differences (p > 0.05). Conclusions: The combination of the iPhone’s TrueDepth camera and the EM3D app exhibited high accuracy for 3D facial modeling. This makes it a cost-effective alternative to professional scanning systems.