Data-Driven Decisions: A Systematic Review of Artificial Intelligence and Machine Learning in Cleft Orthognathic Surgery

Abstract

Introduction: In recent times, there has been a growing interest in the integration of artificial intelligence (AI) and machine learning (ML) into the realm of cleft orthognathic surgery, presenting an exciting avenue for transformative innovations. These technologies offer the promise of optimizing treatment plans, facilitating surgical decision-making, and contributing to a more patient-centric approach. However, a systematic and in-depth exploration of the existing literature is essential to discern the true impact, challenges, and potential future directions of AI and ML in this specialized field. The present systematic review aimed to provide an overview of AI and ML algorithms and their applications in cleft orthognathic surgery. Methodology: A comprehensive search was conducted in databases using MeSH terms and other relevant terms including PubMed, Embase, and Scopus until January 2024. This systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Results: The search strategy resulted in a total of 124 articles. After applying the inclusion and exclusion criteria, a total of 5 studies were included for final review. AI has profoundly impacted the prediction of the need for orthognathic surgeries in cleft patients using cephalometric variables with a clinically acceptable accuracy range. Also, provide guidelines to determine the amount and direction of movements of the maxilla and mandible. Conclusions: Understanding the role of AI and ML in cleft orthognathic surgery is paramount for clinicians, researchers, and policymakers alike.AI reduces the work burden of the clinician by eliminating the tedious registration procedures, thereby helping in efficient and automated planning.

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