@article{deOliveira2024,

title = {Artificial intelligence as a prediction tool for orthognathic surgery assessment},

author = {Pedro Henrique José de Oliveira and Tengfei Li and Haoyue Li and João Roberto Gonçalves and Ary Santos-Pinto and Luiz Gonzaga Gandini Junior and Lucia Soares Cevidanes and Claudia Toyama and Guilherme Paladini Feltrin and Antonio Augusto Campanha and Melchiades Alves de Oliveira Junior and Jonas Bianchi},

url = {https://doi.org/10.1111/ocr.12805},

doi = {10.1111/ocr.12805},

issn = {1601-6335},

year  = {2024},

date = {2024-04-21},

journal = {Orthodontics & Craniofacial Research},

volume = {27},

issue = {5},

pages = {785-794},

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.},

keywords = {artificial intelligence, Class II, Class III, orthodontics, Orthognathic Surgery},

pubstate = {published},

tppubtype = {article}

}

@article{Parizotto2020,

title = {"Can palatal splint improve stability of segmental Le Fort I osteotomies?."},

author = {O L P Juliana and T B Karina and P P Adriano and J Bianchi and S C Daniel and R G Joao },

url = {https://pubmed.ncbi.nlm.nih.gov/32533749/},

doi = {10.1111/ocr.12399},

year  = {2020},

date = {2020-11-00},

urldate = {2020-11-00},

journal = {Orthodontics & Craniofacial Research},

volume = {23},

number = {4},

pages = {486-492},

abstract = {The purpose of this study was to evaluate the influence of a palatal splint on stability in multi-segment maxillary osteotomies.},

keywords = {le fort, Orthognathic Surgery, osteotomy, segmental Le Fort I Osteotomy, stability},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2018,

title = {Three-dimensional stability analysis of maxillomandibular advancement surgery with and without articular disc repositioning},

author = {Bianchi J and M P Guilherme and K Leonardo and I Jaqueline and M W Larry and R G Joao },

year  = {2018},

date = {2018-08-00},

urldate = {2018-08-00},

journal = {J Craniomaxillofacial Surgery},

volume = {46},

number = {8},

pages = {1348-1354},

abstract = {This retrospective cohort study aimed to assess, three-dimensionally, mandible and maxilla changes following maxillomandibular advancement (MMA), with and without repositioning of TMJ articular discs. The sample comprised cone-beam computed tomography data from 32 subjects: group 1 (n = 12) without disc displacement and group 2 (n = 20) with bilateral disc repositioning. An automatic cranial base superimposition method was used to register the images at three time points: T1 (preoperative), T2 (postoperative), and T3 (at least 11 months follow-up). To assess surgical changes (T2-T1) and adaptive responses (T3-T2), the images were compared quantitatively and qualitatively using the shape correspondence method. The results showed that surgical displacements were similar in both groups for all the regions of interest except the condyles, which moved in opposite directions - group 1 to superior and posterior positions, and group 2 to inferior and anterior positions. For adaptive responses, we observed high individual variability, with lower variability in group 2. Sagittal relapse was similar in both groups. In conclusion, there were no significant differences in skeletal stability between the two groups. The maxillomandibular advancement surgeries, with rotation of the occlusal plane, had stable results for both groups immediately after surgery and at 1-year follow-up.},

keywords = {Cone-beam computed tomography, imaging, Orthognathic Surgery, Temporomandibular Joint Disc, three-dimensional},

pubstate = {published},

tppubtype = {article}

}