Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.
Background: The aim of this systematic review is to compare the efficacy of convolutional neural networks (CNN) and Vision Transformers (ViT) in the field of dental imaging, in order to examine in depth the potential, advantages, and limitations of both models in this domain. Methods: The search str...
| Publicado en: | BMC Oral Health Vol. 25; no. 1; pp. 1 - 15 |
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| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
BioMed Central
10/15/2025
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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=188684310&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188684310 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726831 1CIC jtl: BMC Oral Health issn: 14726831 maglogo: N pubinfo: dt: 10/15/2025 vid: 25 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 188684310 188684310 188684310 10.1186/s12903-025-07036-5 188684310 ppf: 1 ppct: 14 formats: tig: atl: Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review. aug: au: Felek, Turgut Tercanlı, Hümeyra Gök, Rümeysa Şendişçi affil: https://ror.org/01m59r132 Institute of Natural and Applied Sciences, Akdeniz University, Antalya, Türkiye sug: subj: Neural Networks (Computer) Evaluation Deep Learning Evaluation Convolutional Neural Networks Evaluation Radiography, Dental Image Processing, Computer Assisted Human Systematic Review Radiography, Panoramic Descriptive Statistics PubMed Checklists Sensitivity and Specificity ab: Background: The aim of this systematic review is to compare the efficacy of convolutional neural networks (CNN) and Vision Transformers (ViT) in the field of dental imaging, in order to examine in depth the potential, advantages, and limitations of both models in this domain. Methods: The search strings used in the study were "(("Vision Transformer" OR ViT OR "Transformer architecture") AND ("Convolutional Neural Network" OR CNN OR ConvNet) AND (Dental OR Dentistry OR "Maxillofacial" OR "Oral Radiology") AND (Image OR Imaging OR Radiograph))". The search was conducted in January 2025. Two investigators independently evaluated the full texts of all eligible articles and excluded those that did not meet the inclusion/exclusion criteria. Results: Of 2596 articles, 21 met the inclusion criteria. Depending on the task category, of the 21 studies that were reviewed, 14 (66.7%) utilized classification, while 7 (33.3%) utilized segmentation. Panoramic radiography is the most commonly used imaging modality (52.3%) and the ViT-based model was observed to have the highest performance (58%). Conclusion: ViT-based deep learning models tend to exhibit higher performance in many dental image analysis scenarios compared to traditional convolutional neural networks. However, in practice CNN and ViT approaches can be used in a complementary manner. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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