Automated Evaluation of Upper Airway Obstruction Based on Deep Learning.
Biomed Res Int
; 2023: 8231425, 2023.
Article
em En
| MEDLINE
| ID: mdl-36852295
ABSTRACT
Objectives:
This study is aimed at developing a screening tool that could evaluate the upper airway obstruction on lateral cephalograms based on deep learning.Methods:
We developed a novel and practical convolutional neural network model to automatically evaluate upper airway obstruction based on ResNet backbone using the lateral cephalogram. A total of 1219 X-ray images were collected for model training and testing.Results:
In comparison with VGG16, our model showed a better performance with sensitivity of 0.86, specificity of 0.89, PPV of 0.90, NPV of 0.85, and F1-score of 0.88, respectively. The heat maps of cephalograms showed a deeper understanding of features learned by deep learning model.Conclusion:
This study demonstrated that deep learning could learn effective features from cephalograms and automated evaluate upper airway obstruction according to X-ray images. Clinical Relevance. A novel and practical deep convolutional neural network model has been established to relieve dentists' workload of screening and improve accuracy in upper airway obstruction.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Obstrução das Vias Respiratórias
/
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
Biomed Res Int
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China