A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.
This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2559 - 2581 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research Journal Article |
| Publicado: |
Springer Nature
Oct2024
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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=181515398&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515398 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515398 181515398 181515398 10.1007/s10278-024-01086-x 181515398 ppf: 2559 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System. aug: au: Lubbad, Mohammed A. H. Kurtulus, Ikbal Leblebicioglu Karaboga, Dervis Kilic, Kerem Basturk, Alper Akay, Bahriye Nalbantoglu, Ozkan Ufuk Yilmaz, Ozden Melis Durmaz Ayata, Mustafa Yilmaz, Serkan Pacal, Ishak affil: https://ror.org/047g8vk19 Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey sug: subj: Deep Learning Dental Implants Decision Support Systems, Clinical Dental Implantation Human Comparative Studies Convolutional Neural Networks Periodontics Radiography, Panoramic Radiography, Dental Funding Source ab: This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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