Revealing the potential of 3D facial imaging : exploring feasibility, reliability, and clinical applications in craniofacial practice

Abstract

Facial morphology analysis is crucial in craniofacial practice, and the adoption of three-dimensional (3D) face acquisition has uncovered significant possibilities for enhanced precision and accuracy. However, incorporating 3D facial imaging technology into clinical practice can be challenging, with varying costs and hardware and software characteristics. This thesis aimed to explore the feasibility, reliability, and clinical applications of 3D facial imaging in craniofacial practice, addressing the research gaps identified in the literature. The thesis commences with a thorough review of existing literature on 3D surface imaging technologies and their use in acquiring 3D facial data for craniofacial research and clinical practice. This review offers updated information on these technologies and systems to aid clinicians in selecting an optimal 3D face acquisition system for their needs. Building on the findings of this review and acknowledging the potential ofsmartphone-generated 3D facial images (SGI) in craniofacial practice, a prospective clinical trial was conducted to validate the use of SGI for routine clinical applications, with a focus on assessing its accuracy in capturing panfacial coverage. Another prospective study was then conducted to evaluate the validity of SGI in analysing the oronasal region in patients with cleft lip and palate (CLP), with a thorough area-specific assessment of this region. Both investigations demonstrated that SGI had high accuracy in capturing central and flat areas of the face in orthognathic surgery (OS) patients, as well as certain central and flat areas within the oronasal region of patients with CLP. To further explore the clinical applications of 3D facial images, artificial intelligence (AI) was leveraged to enhance the accuracy and reliability of predicting post-surgery profiles. A comprehensive investigation was conducted, which presented the application of 3D facial images through AI-assisted technology to determine post-surgery profiles and the clinical applicability of the newly derived modified chin point (MCP). The findings indicated that the MCP was a reliable and consistent predictor for the post-surgery upper lip (UL) position and moderately reliable for predicting the post-surgery lower lip (LL) position, validating its use as a reliable predictor for post-orthognathic surgery profiles. The outcome of this research provides up-to-date information on available 3D face acquisition systems and demonstrates the accuracy and validity of SGI for region-specific macro-proportional facial assessment in OS patients and macroscopic oronasal analysis in patients with CLP. The findings also reveal that SGI can be used for treatment simulations to aid patient education, where accuracy within 3 mm and 5º may not be critical. Additionally, this research provides evidence supporting the effectiveness of MCP in guiding aesthetic lip positioning in patients undergoing OS. The thesis concludesby acknowledging the promise of 3D facial imaging as a valuable tool for craniofacial practice, with implications for future research and clinical applications