Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?
Angle Orthod
; 94(5): 549-556, 2024 Sep 01.
Article
en En
| MEDLINE
| ID: mdl-39230019
ABSTRACT
OBJECTIVES:
To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. MATERIALS ANDMETHODS:
Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regressionmethods:
multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed.RESULTS:
In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models.CONCLUSIONS:
AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Cefalometría
/
Procedimientos Quirúrgicos Ortognáticos
/
Puntos Anatómicos de Referencia
Límite:
Adolescent
/
Adult
/
Female
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Humans
/
Male
Idioma:
En
Revista:
Angle Orthod
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos