1. Dent J (Basel). 2026 Sep 18;14(9):605. doi: 10.3390/dj14090605.

Artificial Intelligence and Digital Technologies in Orthognathic and 
Reconstructive Maxillofacial Surgery: Data Availability and Evidence Maturity.

Campuzano-Donoso M(1), Fonseca-Lascano YD(2), Galárraga-Taco PF(2), Ubidia-Terán 
JA(2), Flores-Reimundo IA(2), Insuasti-Veintimilla AE(2), Proaño-Hernández 
JS(2), Zapata-Nuñez JP(2), Barrera-Meza JJ(2), Parise-Vasco JM(1), 
Reytor-González C(1).

Author information:
(1)Center for Evidence Ecosystems, Implementation Science, and Decision-Making 
(CIDES), Facultad de Ciencias de la Salud y Bienestar Humano, Universidad 
Tecnológica Indoamérica, Ambato 180150, Ecuador.
(2)Facultad de Ciencias Médicas, de la Salud y de la Vida, Escuela de 
Odontología, Universidad Internacional del Ecuador, Quito 170411, Ecuador.

Orthognathic and reconstructive maxillofacial surgery addresses severe 
dentofacial deformities, post-traumatic defects, and defects following oncologic 
resection. Across the pathway from preoperative imaging and virtual planning to 
soft-tissue prediction and intraoperative guidance, artificial intelligence and 
related digital technologies are increasingly being investigated to support 
decision-making, simulation, and plan transfer. This structured narrative review 
synthesizes evidence across that surgical pipeline and uses data availability as 
an organizing framework for interpreting evidence maturity. Predefined searches 
of PubMed/MEDLINE, Embase, and Scopus identified English-language peer-reviewed 
articles published from January 2018 to April 2026, supplemented by selected 
foundational studies. Within the literature reviewed, automated cephalometric 
and three-dimensional landmark detection has undergone the most extensive 
quantitative evaluation, supported by several systematic reviews and 
meta-analyses and by multicenter retrospective evaluations, including one study 
with independent external test sets. Artificial intelligence-assisted diagnosis, 
osteotomy planning, and virtual surgical planning show increasing technical 
capability, including low-millimeter reposition-vector prediction, although 
external validation and patient-centered outcomes remain limited. Soft-tissue 
prediction has advanced through deep learning and finite-element modeling, with 
selected studies reporting comparable geometric accuracy and substantially 
faster computation. In contrast, intraoperative augmented reality, navigation, 
and robot-assisted craniomaxillofacial surgery remain supported mainly by small 
clinical series, cadaveric studies, and preclinical validation. These 
technologies demonstrate plan-transfer feasibility in selected settings but do 
not yet establish broad clinical effectiveness or routine superiority over 
established workflows. Overall, evidence is more mature when datasets are 
structured, accessible, and standardized, and less mature when paired 
longitudinal imaging or intraoperative tracking data are sparse. This 
association should be interpreted as an organizing hypothesis rather than proof 
of causality.

DOI: 10.3390/dj14090605
PMCID: PMC13605402
PMID: 42782992

Conflict of interest statement: The authors declare no conflicts of interest.


2. Plast Surg (Oakv). 2026 Aug 31:22925503261478090. doi: 
10.1177/22925503261478090. Online ahead of print.

Patient Perspective on Preoperative Simulation in Plastic Surgery: A Systematic 
Review of Patient-Reported Outcomes.

El Sewify O(1), Gorgy A(2), Sylvain M(3), Legler J(4), Zammit D(2).

Author information:
(1)Faculty of Medicine, Laval University, Québec City, Quebec, Canada.
(2)Division of Plastic and Reconstructive Surgery, McGill University Health 
Center, Montreal, Quebec, Canada.
(3)Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, 
Canada.
(4)Division of Orthopaedic Surgery, McGill University Health Center, Montreal, 
Quebec, Canada.

INTRODUCTION: Preoperative simulation technologies are increasingly utilized in 
plastic surgery to enhance surgical planning and patient communication. While 
their technical benefits have been established, the impact on patient-reported 
outcomes (PROs), in particular satisfaction and perceived accuracy, remain 
underexplored. This systematic review evaluated the effect of preoperative 
simulation on patient-reported satisfaction, decision-making, and perceived 
accuracy in breast and facial aesthetic plastic surgery.
METHODS: A systematic search of MEDLINE, Embase, and Cochrane databases up to 
April 2025 identified studies reporting PROs in adult patients undergoing breast 
(aesthetic or reconstructive) or facial aesthetic surgery with preoperative 
simulation. Data on demographics, simulation type, satisfaction, accuracy, and 
complications were extracted and analyzed.
RESULTS: Seventeen studies (1333 patients; 970 simulation, 363 control) met 
inclusion criteria. Breast surgery patients using simulation showed higher 
satisfaction with appearance (Breast-Q: 75.0 vs 58.1), surgical outcomes (84.9% 
vs 78.8%), and psychosocial well-being. Simulation patients reported higher 
satisfaction with the simulation experience (95.4%) and greater trust in their 
surgeon (95.2%), with lower complication rates (4.1% vs 41.5%). In facial 
surgery, simulation users demonstrated greater overall satisfaction (79.2% vs 
67.3%) and improved understanding of outcomes. Perceived accuracy was higher in 
breast (91.0%) than facial surgery (74.1%). Across procedures, simulation 
enhanced shared decision-making and surgeon-patient rapport.
CONCLUSION: Current evidence suggests that preoperative simulation may enhance 
patient satisfaction, trust, and engagement in plastic surgery, particularly in 
breast procedures. However, findings are based on heterogeneous studies with 
diverse patient populations, simulation technologies, and outcome measures. 
Further prospective comparative studies using standardized PRO measures are 
needed to better define the role of preoperative simulation across aesthetic and 
reconstructive plastic surgery.

Publisher: INTRODUCTION: Les technologies de simulation préopératoire sont de 
plus en plus utilisées en chirurgie plastique pour améliorer la planification de 
l’intervention et la communication avec les patients. Leurs avantages techniques 
sont bien établis, mais leurs effets sur les résultats déclarés par les patients 
(RDP), notamment la satisfaction et la perception de précision, demeurent peu 
étudiés. La présente analyse systématique a permis d’évaluer l’effet de la 
simulation préopératoire sur la satisfaction déclarée par les patients, le 
processus décisionnel et la perception de précision en chirurgie esthétique 
mammaire et faciale.
MÉTHODES: Après une fouille systématique des bases de données MEDLINE, Embase et 
Cochrane jusqu’en avril 2025, les chercheurs ont recensé les études abordant les 
résultats déclarés par des patients adultes ayant subi une chirurgie mammaire 
(esthétique ou reconstructive) ou une chirurgie esthétique faciale conjuguées à 
une simulation préopératoire. Ils ont extrait et analysé les données sur les 
caractéristiques démographiques, le type de simulation, la satisfaction, la 
précision et les complications.
RÉSULTATS: Au total, 17 études (1 333 patients; 970 incluant la simulation, 363 
sujets-témoins) respectaient les critères d’inclusion. Les patientes qui avaient 
subi une chirurgie mammaire après une simulation étaient plus satisfaites de 
leur apparence (Breast-Q : 75,0 par rapport à 58,1), de leurs résultats 
chirurgicaux (84,9 % par rapport à 78,8 %) et de leur bien-être psychosocial. 
Celles qui avaient été exposées à la simulation étaient satisfaites de 
l’expérience de simulation (95,4 %), avaient plus confiance en leur chirurgien 
(95,2 %) et présentaient des taux de complications plus faibles (4,1 % par 
rapport à 41,5 %). En chirurgie faciale, les personnes exposées à la simulation 
présentaient une meilleure satisfaction globale (79,2 % par rapport à 67,3 %) et 
comprenaient mieux les résultats attendus. La perception de précision était plus 
élevée en chirurgie mammaire (91,0 %) qu’en chirurgie faciale (74,1 %). Toutes 
interventions confondues, la simulation a favorisé des décisions communes et la 
relation entre le chirurgien et les patients.
CONCLUSION: Selon les données actuelles, la simulation préopératoire pourrait 
améliorer la satisfaction, la confiance et la participation des patients en 
chirurgie plastique, particulièrement dans le cadre d’interventions mammaires. 
Ces résultats reposent toutefois sur des études hétérogènes aux populations de 
patients, aux technologies de simulation et aux mesures de résultats 
diversifiés. D’autres études prospectives comparatives faisant appel à des 
mesures normalisées des RDP devront être réalisées pour mieux définir le rôle de 
la simulation préopératoire en chirurgie esthétique et reconstructive.

© 2026 The Author(s).

DOI: 10.1177/22925503261478090
PMCID: PMC13530028
PMID: 42682868

Conflict of interest statement: The authors declared no potential conflicts of 
interest with respect to the research, authorship, and/or publication of this 
article.


3. J Pers Med. 2026 Aug 13;16(8):427. doi: 10.3390/jpm16080427.

Prediction of Three-Dimensional Soft Tissue Changes Following Mandibular PEEK 
Implantation (Proof of Concept).

Zwijnenberg TL(1), Schepers RH(2), Jansma J(2), Glas HH(1).

Author information:
(1)Clinic for Virtual Surgical Planning, 3D VSP B.V., Hoogwatum 2, 9906 TD 
Bierum, Groningen, The Netherlands.
(2)Department of Oral and Maxillofacial Surgery, Expertcenter for Orthofacial 
Surgery, Martini Hospital Groningen, Van Swietenplein 1, 9728 NT Groningen, 
Groningen, The Netherlands.

Background/Objectives: Polyetheretherketone (PEEK) implants are increasingly 
being used for mandibular reconstruction and augmentation, yet predicting the 
resulting facial soft tissue changes remains challenging. Accurate pre-operative 
prediction of soft tissue deformation is essential for surgical planning and 
patient counselling. To the best of our knowledge, this study presents the first 
application of a computational model for predicting three-dimensional facial 
soft tissue deformation induced by PEEK implants. Methods: Four patient cases 
with pre-operative and post-operative cone-beam computed tomography scans were 
analysed. A mass tensor model was employed to predict soft tissue changes based 
on the pre-operative soft tissue shape, mandibular anatomy, and the designed 
PEEK implant. Model accuracy was quantified by comparing predicted outcomes to 
post-operative scans using landmark-based analysis of chin and jaw positions, as 
well as surface distance mapping, with a mean error below 2 mm adopted as the 
criterion for clinically acceptable accuracy. Results: Landmark-based analysis 
revealed a mean absolute error of 0.9 ± 0.8 mm between the predicted and actual 
positions. The mean surface distances were 1.3 mm for the jaw-chin region and 
1.0 mm for the entire facial region. Conclusions: These results demonstrate the 
potential of a computational approach for real-time prediction of facial soft 
tissue changes following PEEK implant placement, achieving clinically relevant 
accuracy at the cohort level, with a tendency towards under-prediction and one 
case exceeding the threshold.

DOI: 10.3390/jpm16080427
PMCID: PMC13514417
PMID: 42645921

Conflict of interest statement: Tom L. Zwijnenberg is employed as a researcher 
at 3D VSP. Haye H. Glas is employed as a Technical Physician at 3D VSP and holds 
equity in the company. 3D VSP commercially designed and supplied the PEEK 
implants in this study to the hospital where the patients were treated. The rest 
of the authors of this article declare no conflicts of interest with respect to 
the research.