Shaping the 4D frontier in maxillofacial surgery with faceMesh evolution

• Published in: Journal of stomatology, oral and maxillofacial surgery Vol. 125; no. 3; p. 101843
• Main Authors: Grillo, Ricardo, Reis, Bruno Alvarez Quinta, Lima, Bernardo Correia, Melhem-Elias, Fernando
• Format: Journal Article
• Language: English
• Published: France Elsevier Masson SAS 01.06.2024
• Subjects: Algorithms; Anatomic Landmarks; Artificial Intelligence; Digital planning; Face - surgery; Facial recognition; Humans; Imaging, Three-Dimensional - methods; Neural Networks, Computer; Oral Surgical Procedures - methods; Orthognathic surgery; Software; Surgery, Computer-Assisted - methods; Surgery, Oral - methods; Virtual planning; Orthognathic surgery; Virtual planning; Artificial intelligence; Digital planning; Facial recognition
• ISSN: 2468-7855; 2468-7855
• DOI: 10.1016/j.jormas.2024.101843

This work aims to introduce a Python-based algorithm and delve into the recent paradigm shift in Maxillofacial Surgery propelled by technological advancement. The provided code exemplifies the utilization of the MediaPipe library, created by Google in C++, with an additional Python interface available as a binding. The advent of FaceMesh coupled with artificial intelligence (AI), has brought about a transformative wave in contemporary maxillofacial surgery. This cutting-edge deep neural network, seamlessly integrated with Virtual Surgical Planning (VSP), offers surgeons precise 4D facial mapping capabilities. It accurately identifies facial landmarks, tailoring surgical interventions to individual patients, and streamlining the overall surgical procedure. FaceMesh emerges as a revolutionary tool in modern maxillofacial surgery. This deep neural network empowers surgeons with detailed insights into facial morphology, aiding in personalized interventions and optimizing surgical outcomes. The real-time assessment of facial dynamics contributes to improved aesthetic and functional results, particularly in complex cases like facial asymmetries or reconstructions. Additionally, FaceMesh has the potential for early detection of medical conditions and disease prediction, further enhancing patient care. Ongoing refinement and validation are essential to address limitations and ensure the reliability and effectiveness of FaceMesh in clinical settings.