Abstract
A systematic review was conducted in early 2019 to evaluate the articles published that dealt with digital workflow, CAD, rapid prototyping and digital image processing in the rehabilitation by maxillofacial prosthetics. The objective of the review was to primarily identify the recorded cases of orofacial rehabilitation made by maxillofacial prosthetics using computer assisted 3D printing. Secondary objectives were to analyze the methods of data acquisition recorded with challenges and limitations documented with various software in the workflow. Articles were searched from Scopus, PubMed and Google Scholar based on the predetermined eligibility criteria. Thirty-nine selected papers from 1992 to 2019 were then read and categorized according to type of prosthesis described in the papers. For nasal prostheses, Common Methods of data acquisition mentioned were computed tomography, photogrammetry and laser scanners. After image processing, computer aided design (CAD) was used to design and merge the prosthesis to the peripheral healthy tissue. Designing and printing the mold was more preferred. Moisture and muscle movement affected the overall fit especially for prostheses directly designed and printed. For auricular prostheses, laser scanning was most preferred. For unilateral defects, CAD was used to mirror the healthy tissue over to the defect side. Authors emphasized on the need of digital library for prostheses selection, especially for bilateral defects. Printing the mold and conventionally creating the prosthesis was most preferred due to issues of proper fit and color matching. Orbital prostheses follow a similar workflow as auricular prosthesis. 3D photogrammetry and laser scans were more preferred and directly printing the prosthesis was favored in various instance. However, ocular prostheses fabrication was recorded to be a challenge due to difficulties in appropriate volume reconstruction and inability to mirror healthy globe. Only successful cases of digitally designed and printed iris were noted. (C) 2019 Elsevier Masson SAS. All rights reserved.