de Oliveira PHJ, Li T, Li H, Gonçalves JR, Santos-Pinto A, Gandini LG, Cevidanes LS, Toyama C, Feltrin GP, Campanha AA, de Oliveira MA, Bianchi J. Artificial intelligence as a prediction tool for orthognathic surgery assessment.
Orthod Craniofac Res 2024;
27:785-794. [PMID:
38715428 PMCID:
PMC11789623 DOI:
10.1111/ocr.12805]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2024] [Indexed: 10/11/2024]
Abstract
INTRODUCTION
An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values.
METHODS
A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2).
RESULTS
The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89).
CONCLUSIONS
The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.
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