A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities.
Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1824 - 1846 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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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=179554141&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554141 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554141 179554141 179554141 10.1007/s10278-024-01054-5 179554141 ppf: 1824 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities. aug: au: Zanini, Luiz Guilherme Kasputis Rubira-Bullen, Izabel Regina Fischer Nunes, Fátima de Lourdes dos Santos affil: https://ror.org/036rp1748 Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, 05508-010, São Paulo, São Paulo, Brazil sug: subj: Dental Caries Diagnosis Dental Caries Classification Radiography, Dental X-Rays Technology, Dental Image Processing, Computer Assisted Human Systematic Review PubMed Machine Learning Dental Caries Radiography Dentistry Descriptive Statistics Comparative Studies Funding Source ab: Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field. pubtype: Academic Journal doctype: equations & formulas pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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