| Description: |
Objective Orthognathic surgery is a transformative solution for skeletal Class III malocclusion. This review explores factors affecting skeletal, dental, and soft tissue changes in these patients and examines the integration of machine learning (ML) in treatment management to uncover patterns within large datasets. Materials and Methods Studies were selected from Google Scholar, PubMed, and ScienceDirect using keywords such as "(affecting factor OR predisposition) AND (skeletal class III OR skeletal class 3) AND (orthognathic surgery OR surgical orthodontics)," "Morphology changes in skeletal class III orthognathic surgery," "Machine learning in dentistry," and "Machine learning in orthodontics." Most reviewed articles were published in the last five years. Results Artificial intelligence (AI) has significantly impacted medicine and dentistry, including orthognathic surgery. Integrating AI and ML, such as support vector machines (SVMs), decision trees (DTs), random forests (RFs), artificial neural networks (ANNs), and convolutional neural networks (CNNs), has advanced diagnosis, treatment planning, and outcome prediction. AI and ML enhance various medical analyses, optimize treatment procedures, and predict long-term outcomes. In diagnosis, they assist in skeletal classification, cephalometric analysis, and determining orthodontic surgery needs. For treatment planning, they aid in orthodontic treatment and simulate orthognathic surgery. In outcome prediction, they forecast post-treatment facial morphology, assess pharyngeal anatomy, and predict treatment stability. Conclusion Orthognathic surgery, combined with ML and AI, enhances treatment management for skeletal Class III malocclusion. These technologies improve diagnostic accuracy, optimize treatment procedures, and enhance post-treatment assessments. Ongoing AI advancements promise superior diagnoses, treatment planning, and outcomes in orthodontics and orthognathic surgery. |