Intelligent Computing Models for Predicting Surgical and Functional Outcomes in Cleft Lip and Palate: A Comprehensive Review

DOI

10.2991/978-94-6239-654-8_13How to use a DOI?

Keywords

Intelligent computing; machine learning; deep learning; cleft lip and palate; outcome prediction; surgical planning; facial analysis; velopharyngeal function

Abstract

Intelligent computing has rapidly advanced as a powerful tool for enhancing clinical decision-making in cleft lip and palate (CLP) management. With the emergence of machine learning, deep learning, and multimodal data integration, predictive modelling has become increasingly capable of identifying subtle anatomical, functional, and developmental patterns that traditional clinical assessments often miss. These computational approaches enable objective prediction of postoperative aesthetic outcomes, velopharyngeal function, maxillofacial growth, and the likelihood of secondary surgical interventions. Automated three-dimensional facial analysis, virtual surgical planning, and interpretable artificial intelligence provide clinicians with reproducible and transparent insights that strengthen treatment planning and patient counselling. As neuromorphic computing, foundation models, and collaborative human–AI systems continue to evolve, intelligent computing is poised to become an essential component of personalized cleft care. Its integration promises improved accuracy, reduced subjectivity, and enhanced long-term outcome prediction, ultimately supporting more precise and patient-centred approaches to CLP management.

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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TY  - CONF
AU  - Vijay Ebenezer
AU  - Pradeepa Ganesh
AU  - Arjun Arjun
AU  - Parijat Parijat
AU  - Atharva Atharva
AU  - Akilan Akilan
PY  - 2026
DA  - 2026/04/24
TI  - Intelligent Computing Models for Predicting Surgical and Functional Outcomes in Cleft Lip and Palate: A Comprehensive Review
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 140
EP  - 151
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_13
DO  - 10.2991/978-94-6239-654-8_13
ID  - Ebenezer2026
ER  -