RESUMEN
OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. METHODS: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. RESULTS: The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. CONCLUSIONS: YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.
Asunto(s)
Aprendizaje Profundo , Dentición Mixta , Odontología Pediátrica , Radiografía Panorámica , Diente , Radiografía Panorámica/métodos , Aprendizaje Profundo/normas , Diente/diagnóstico por imagen , Humanos , Preescolar , Niño , Adolescente , Masculino , Femenino , Odontología Pediátrica/métodosRESUMEN
PURPOSE: The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers. METHODS: Hand-wrist radiographs (HWRs) from 1067 individuals aged between 7 and 18 years were included. Fifteen regions of interest were selected for fractal dimension (FD) analysis. Five predictive models with different inputs were created (model 1: only FD; model 2: FD and Chapman sesamoid stage; model 3: FD, age, and sex; model 4: FD, Chapman sesamoid stage, age, and sex; model 5: Chapman sesamoid stage, age, and sex). The target diagnoses were accelerating growth velocity, very high growth velocity, and decreasing growth velocity. Four AI algorithms were applied: multilayer perceptron (MLP), support vector machine (SVM), gradient boosting machine (GBM) and C 5.0 decision tree classifier. RESULTS: All AI algorithms except for C 5.0 yielded similar overall predictive accuracies for the five models. In order from lowest to highest, the predictive accuracies of the models were as follows: model 1â¯< model 3â¯< model 2â¯< model 5â¯< model 4. The highest overall F1 score, which was used instead of accuracy especially for models with unbalanced data, was obtained for models 1, 2, and 3 based on SVM, for model 4 based on MLP, and for model 5 based on C 5.0. Adding Chapman sesamoid stage, chronologic age, and sex as additional inputs to the FD values significantly increased the F1 score. CONCLUSION: Applying FD analysis to HWRs is not sufficient to predict maturation stage in growing patients but can be considered a growth rate prediction method if combined with the Chapman sesamoid stage, age, and sex.
Asunto(s)
Determinación de la Edad por el Esqueleto , Inteligencia Artificial , Fractales , Humanos , Niño , Masculino , Femenino , Adolescente , Determinación de la Edad por el Esqueleto/métodos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Algoritmos , Mano/diagnóstico por imagen , Interpretación de Imagen Radiográfica Asistida por Computador/métodosRESUMEN
OBJECTIVES: This study investigated differences in trabecular structure and mandibular cortical thickness in adults related to vertical facial type (VFT), sex, and their interactions. STUDY DESIGN: Lateral cephalometric radiographs (LCRs) and panoramic radiographs (PRs) of 256 patients were reviewed. The VFT classification into low-angle, normal, and high-angle groups was determined using angular and linear measurements on LCRs. Fractal dimension (FD) values and mandibular radiomorphometric indices (RMIs) were calculated on PRs. RESULTS: Two-way analysis of variance revealed significant differences in FD overall among VFT groups in all sites (P < .001), with pairwise comparisons indicating the greatest values in the high-angle group in the condyle (P < .05) but in the low-angle group elsewhere (P < .001). RMIs were significantly different overall regarding VFT only in the posterior mandible (P = .004), with pairwise comparisons revealing low-angle and normal group values greater than high-angle group values (P < .05). Patient sex and the interaction of facial type and sex had no significant effect on any bone measurements. CONCLUSIONS: VFT had significant effects on trabecular structure at all measured sites, but cortical thickness was affected only in 1 location.