Deep convolutional neural network—the evaluation of cervical vertebrae maturation.
Objectives: This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages us...
| Published in: | Oral Radiology Vol. 39; no. 4; pp. 629 - 639 |
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| Main Authors: | , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Oct2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171992189&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171992189 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09116028 1P9 jtl: Oral Radiology issn: 09116028 maglogo: N pubinfo: dt: Oct2023 vid: 39 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171992189 162308195 171992189 171992189 10.1007/s11282-023-00678-7 171992189 ppf: 629 ppct: 10 formats: tig: atl: Deep convolutional neural network—the evaluation of cervical vertebrae maturation. aug: au: Akay, Gülsün Akcayol, M. Ali Özdem, Kevser Güngör, Kahraman affil: https://ror.org/054xkpr46 Department of Dentomaxillofacial Radiology, Gazi University Faculty of Dentistry, Emek, Ankara, Turkey sug: subj: Cervical Vertebrae Anatomy and Histology Physical Maturation Evaluation Cervical Vertebrae Radiography Neural Networks (Computer) Cephalometry Methods Radiography Methods Human Child Adolescence Young Adult Experimental Studies Models, Statistical Descriptive Statistics Child: 6-12 years Adolescent: 13-18 years ab: Objectives: This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages using precision, recall, and F1-score. Methods: A total of 588 digital lateral cephalometric radiographs of patients with a chronological age between 8 and 22 years were included in this study. CVM evaluation was carried out by two dentomaxillofacial radiologists. CVM stages in the images were divided into 6 subgroups according to the growth process. A convolutional neural network (CNN) model was developed in this study. Experimental studies for the developed model were carried out in the Jupyter Notebook environment using the Python programming language, the Keras, and TensorFlow libraries. Results: As a result of the training that lasted 40 epochs, 58% training and 57% test accuracy were obtained. The model obtained results that were very close to the training on the test data. On the other hand, it was determined that the model showed the highest success in terms of precision and F1-score in the CVM Stage 1 and the highest success in the recall value in the CVM Stage 2. Conclusion: The experimental results have shown that the developed model achieved moderate success and it reached a classification accuracy of 58.66% in CVM stage classification. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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