Artificial intelligence as a prediction tool for orthognathic surgery assessment.

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.

Three-dimensional cone-beam computed technology evaluation of skeletal and dental changes in growing patients with Class II malocclusion treated with the cervical pull face-bow headgear appliance.

INTRODUCTION: This study aimed to 3-dimensionally quantify and compare the outcomes of growing patients with Class II malocclusion treated with the cervical pull face-bow headgear appliance in combination with full fixed orthodontic appliances. METHODS: The study sample consisted of 22 patients with Class II malocclusion with the following inclusion criteria: ANB >4.75°, Class II molar relationship, and SN-GoGn <37°. The mean age of patients was 12.5 ± 1.1 years at baseline. The average treatment time was 27.7 ± 7.3 months. Cone-beam computed tomography scans were superimposed in the cranial base, maxillary regional, and mandibular regional to evaluate growth, treatment displacements, and bone remodeling. RESULTS: Relevant statistically and clinically significant skeletal changes included average decreases in ANB (2.1 ± 1.1°) and SNA (1.8 ± 1.1°); posterior (1.3 ± 1.4 mm) and inferior (4.6 ± 2.2 mm) displacement of A-point; inferior displacements of B-point (5.4 ± 2.8 mm) and Pogonion (5.8 ± 2.6 mm); superior displacement of Condylion (6.9 ± 2.4 mm); increase in mandibular length (5.4 ± 2.0 mm); and clockwise rotation of palatal plane (1.9 ± 1.9°). Significant proclination of the maxillary incisors (9.8 ± 11.1°) and nonsignificant proclination of the mandibular incisors (4.7 ± 9.6°) were also noted. CONCLUSIONS: Class II skeletal correction was primarily achieved by posterior, inferior displacement of the sagittal position of the maxilla. Change in the sagittal position of the mandible/chin (B-point, Pogonion) was not significant; rather, mandibular displacement was significant in an inferior vertical direction without backward rotation, as seen from marked condylar and ramus growth.