Accuracy of artificial intelligence in predicting facial changes post-orthognathic surgery: a comprehensive scoping review

• Asim A. Almarhoumi ^[1]
  1. [1] M.Orth RCSEd. Division of Orthodontics, Department of Preventive Dental Sciences, College of Dentistry and Dental Hospital at Taibah University, Madinah, Saudi Arabia
• Localización: Journal of Clinical and Experimental Dentistry, ISSN-e 1989-5488, Vol. 16, Nº. 5 (May), 2024, págs. 624-633
• Idioma: inglés
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• Resumen

  • Accurate prediction of facial soft tissue changes post-orthognathic surgery is crucial for treatment planning and patient communication. Current models pose limitations due to the complexity of facial biomechanics and individual variances. Artificial intelligence (AI) has emerged as an important tool in many disciplines, including the dental field.

    The aim of this scoping review is to assess the accuracy of AI in predicting facial changes post-orthognathic surgery in comparison to traditional models. Explore the strengths and limitations of the current AI models.

    Following PRISMA-DTA guidelines, a comprehensive search was conducted manually and through Medline, Embase, Web of Science, Scopus, and Google Scholar databases was conducted, focusing on studies that applied AI models with various machine learning and deep learning algorithms for post-surgical outcome prediction. Selection criteria were based on the PICO format, emphasizing studies that compared AI-predicted outcomes with actual post-surgical results. Literature was searched until January 31, 2024.

    The initial search result yielded 1579 records. After screening and assessment for eligibility, seven studies met the inclusion criteria, with publication dates ranging from 2009 to 2023. Several AI algorithms were evaluated on different orthognathic surgical procedures, revealing the high predictive accuracy of AI models across various facial regions.

    AI demonstrates significant potential for enhancing the precision of facial outcome predictions following orthognathic surgery. However, despite the promising results, limitations such as small sample sizes and a lack of external validation were noted. Further research with larger, more diverse datasets and standardized validation methods is essential for optimizing AI’s clinical utility.