To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice. A dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale. The accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85 %, 85 %, and 100 % for the upper and middle thirds of the face, and 30 %, 30 %, and 35 % for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool. The AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication. Using AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication.

Background: In recent years, advancements in digital dentistry have provided new opportunities for more predictable and efficient treatment options, particularly in patients with failing dentition. This study aimed to evaluate the effectiveness and accuracy of a fully digital workflow using stackable surgical templates for complete dental arch rehabilitation with implant-supported fixed restorations. Methods: Four patients, comprising two males and two females with a mean age of 66 years, were included in this case series. Each patient underwent meticulous digital planning, including CBCT and intraoral scanning, to create a virtual patient for preoperative assessment and virtual treatment planning. The assessment of the trueness of implant positioning was conducted in Geomagic Control X software (version 2017.0.3) by referencing anatomical landmarks from both the preoperative and one-year postoperative CBCT scans. Results: A total of 25 dental implants were placed in the maxilla, followed by the installation of long-term provisional restorations. The results showed minimal deviation between the planned and actual implant positions, with mean 3D coronal, apical, and angular discrepancies of 0.87 mm, 2.04 mm, and 2.67°, respectively. All implants achieved successful osseointegration, and no failures were recorded, resulting in a 100% survival rate at the one-year follow-up. Patients reported high satisfaction with both the esthetic and functional outcomes based on their subjective feedback. Conclusions: The findings suggest that the use of a fully digital workflow with stackable surgical templates is a reliable and effective approach for immediate implant placement and prosthetic rehabilitation, enhancing treatment precision and patient comfort.

Static guided surgery for dental implant insertion is a well-documented procedure requiring the manufacturing of a custom-made surgical guide, either teeth-supported, mucosal-supported, bone-supported, or mixed (teeth-mucosal-supported), depending on the clinical situation. The guidance of the surgical drills during implant bed preparation could be undertaken using a sequence of different diameters of metal drill sleeves or, with the sleeves incorporated in the surgical guide, shank-modified drills, both clinically accepted and used with good results. Despite the great number of advantages associated with the use of guided surgery, one of the major risks is guide fracture during drilling for implant bed preparation. Therefore, the aim of the present study was to evaluate the surgical guides without metal sleeves and to simulate, with the aid of Finite Element Analysis (FEA), the use of such dentally supported guides for implant insertion. The FEA is performed in CATIA v5 software after defining the surgical guide mesh material and bone properties. A maximum stress of 6.92 MPa appeared on the guide at the special built-in window meant to allow cooling during drilling, and the maximum value of the guide displacement during drilling simulation was 0.002 mm. Taking into consideration the limits of the current research, the designed tooth-supported surgical guide can withstand the forces occurring during the surgery, even in denser bone, without the risk of fracture.

The treatment of maxillofacial defects presents significant challenges due to the complexity of facial anatomy and the diversity of affected tissues. Traditional workflows are labor-intensive, costly, and limited in customization. Recent advancements in fully digital workflows and direct 3D printing technologies offer new possibilities for improving the fit, aesthetics, and efficiency of prosthetic manufacturing. This scoping review aims to evaluate the current state of direct 3D printing for maxillofacial soft prostheses, assess material properties and biocompatibility, and identify challenges and future directions in this field. Methods: A comprehensive search of PubMed and Scopus databases, along with a manual search of relevant journals, was conducted to identify studies published up to December 2024. Articles focusing on direct 3D printing of maxillofacial soft prostheses were included, while studies involving traditional or mold-based workflows, ocular prostheses, and literature reviews were excluded. Data on materials, manufacturing techniques, and clinical outcomes were extracted and analyzed. Results: Out of 898 articles screened, 11 were included, 5 of which were in vivo studies (case reports). The additive manufacturing methods used in these case reports were Drop-on-Demand (DoD) silicone printing and PolyJet technology. Conclusions: Fully digital workflows and direct 3D printing technologies show promise for advancing maxillofacial prosthesis manufacturing. However, the absence of dedicated software, biocompatible materials, and medium- to long-term clinical evaluations highlight significant research gaps. Future research should focus on material development, workflow optimization, and clinical validation to enable widespread clinical adoption.

This scoping review aims to evaluate the extent of artificial intelligence (AI)- driven automation currently available for designing static surgical guides (SG) for implant placement and its correlation with implant placement accuracy. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, Embase, Cochrane Library, and grey literature up to February 2025. Two reviewers independently screened 518 English-language articles, selecting 140 for eligibility evaluation. After applying exclusion criteria, seven studies were included for full-text review. The SG planning process was classified as manual, semi-automated, or fully automated driven by AI. Implant placement accuracy was assessed based on linear (cervical and apical) and angular deviations, with a detailed review of measurement methods used. Additionally, a market survey was conducted to identify available SG design software, its key features, and the level of automation implemented for each design step. All included studies (n=7) employed a semi-automated software for SG design. The mean deviations in implant placement using SGs were 0.65 mm (0.22 to 1.19 mm) (linear-cervical), 0.95 mm (0.18 to 2.11 mm) (linear-apical), and 2.92° (0.77 to 6.35°) (angular). The software programs used were: coDiagnostiX™ software (Version 9.0, Dental Wings GmbH, Germany), Smop-software (version 2.7.0, Swissmeda AG, Switzerland), 3Shape Implant studio (Version 2021.1.2, 3Shape, Denmark), R2WARE™ (MegaGen implant, Korea), 3-Matic modelling software (Materialise, Belgium) and Blue Sky Plan 4.8 (Blue Sky Bio, USA). This scoping review found that most surgical guide planning software employs a semi-automated approach requiring human intervention, which has shown clinically acceptable implant placement accuracy. Fully automated (AI-based) designs were not yet validated scientifically.

Background/Objectives: Accurate three-dimensional (3D) facial scanning is increasingly important in digital dentistry for diagnosis, treatment planning, and virtual patient creation. Multiple facial scanning technologies are available; however, their metric reliability varies depending on acquisition principles and anatomical orientation. This study aimed to evaluate the trueness, orientation-dependent performance (vertical midline versus horizontal facial measurements), and scanning time of four facial scanning technologies using calibrated manual anthropometry as the reference standard. Methods: Thirty dentate adult participants received adhesive fiducial markers on five predefined facial landmarks. Four linear facial distances were measured clinically using a digital caliper and compared with corresponding measurements obtained from standardized 3D facial scans. Digital measurements were extracted following uniform metric normalization. Inter-examiner reliability, measurement trueness, orientation-related differences, and scanning time were analyzed. Results: Inter-examiner reliability was excellent for both clinical and digital measurements (ICC > 0.93). All facial scanning technologies significantly overestimated manual distances (p < 0.001). The structured-light scanning system showed the smallest deviations (typically <1 mm) and the highest overall accuracy, followed by the depth-fusion system, while photogrammetry-based and NeRF-based approaches demonstrated larger errors, frequently exceeding 2–3 mm. Horizontal facial distances consistently showed greater deviations than vertical midline measurements across all systems. Scanning time differed significantly between technologies, with passive image-based approaches being the fastest and NeRF-based acquisition requiring the longest capture time. Conclusions: Active structured-light facial scanning demonstrated the highest trueness for linear facial anthropometry, whereas passive photogrammetry and NeRF-based approaches showed lower metric trueness and are currently more suitable for educational applications.