Automated Evaluation of Upper Airway Obstruction Based on Deep Learning.
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 R...
| Publicado en: | BioMed Research International pp. 1 - 8 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/18/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161966945&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161966945 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/18/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 161966945 161966945 161966945 10.1155/2023/8231425 161966945 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Evaluation of Upper Airway Obstruction Based on Deep Learning. aug: au: Jeong, Yunho Nang, Yeeyeewin Zhao, Zhihe affil: State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan 610041, China sug: subj: Airway Obstruction Diagnosis Deep Learning Cephalometry Methods Health Screening Human Neural Networks (Computer) Tomography, X-Ray Computed Sensitivity and Specificity Maps ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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