Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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Machine Learning and Computer Vision in Maxillofacial Surgery: Advances in Prediction, Planning, and Automation

Authors
Vijay Ebenezer1, *, Pavishwarya Pavishwarya1, Ashwin Shravana Kumar1, Pawan Chandrakar1, Manasvi Paul1, Nandagopal Nandagopal1
1Department of Oral and Maxillofacial Surgery, Sree Balaji Dental College and Hospital, Bharath Institute of Higher Education and Research, Chennai, India
*Corresponding author. Email: drvijayomfs@yahoo.com
Corresponding Author
Vijay Ebenezer
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_14How to use a DOI?
Keywords
artificial intelligence; surgical planning; automated diagnostics; imaging; Oral and maxillofacial surgery
Abstract

Through machine learning (ML) and computer vision (CV) advances are taking shape in maxillofacial surgery that are revolutionising diagnosis, automation of laborious imaging tasks, and data-driven surgical planning and intraoperative support. New deep learning architectures have been used in three-dimensional imaging modalities including cone-beam computed tomography (CBCT) and CT to segment bone, teeth, mandibular canals, and implants and report similar performance to expert manual labelling and a significant reduction in processing time. In orthognathic and reconstructive workflows, ML-based shape estimation, reference model generation, and virtual surgical planning tools are used for reproducible planning and simulation. Computer-vision-based analysis of surgical videos and instrument tracking are very promising for intraoperative guidance and automated charting while their clinical implementation is constrained by diverse available data and regulatory obstacles. In this narrative review, we summarise new technical breakthroughs, clinical applications, important datasets and benchmarks, and key barriers towards translation from ML/CV to everyday maxillofacial surgical use.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_14How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Vijay Ebenezer
AU  - Pavishwarya Pavishwarya
AU  - Ashwin Shravana Kumar
AU  - Pawan Chandrakar
AU  - Manasvi Paul
AU  - Nandagopal Nandagopal
PY  - 2026
DA  - 2026/04/24
TI  - Machine Learning and Computer Vision in Maxillofacial Surgery: Advances in Prediction, Planning, and Automation
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 152
EP  - 159
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_14
DO  - 10.2991/978-94-6239-654-8_14
ID  - Ebenezer2026
ER  -