Caries detection with tooth surface segmentation on intraoral photographic images using deep learning.
Background: Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the...
| Published in: | BMC Oral Health Vol. 22; no. 1; pp. 1 - 10 |
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| Main Authors: | , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
BioMed Central
12/7/2022
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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=160647431&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160647431 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726831 1CIC jtl: BMC Oral Health issn: 14726831 maglogo: N pubinfo: dt: 12/7/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 160647431 160647431 160647431 10.1186/s12903-022-02589-1 160647431 ppf: 1 ppct: 9 formats: tig: atl: Caries detection with tooth surface segmentation on intraoral photographic images using deep learning. aug: au: Park, Eun Young Cho, Hyeonrae Kang, Sohee Jeong, Sungmoon Kim, Eun-Kyong affil: Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea sug: subj: Dental Caries Diagnosis Deep Learning Algorithms Tooth Photography Image Interpretation, Computer Assisted Human Prospective Studies Photography Equipment and Supplies Dental Clinics Random Assignment Data Management Dental Caries Classification Neural Networks (Computer) ROC Curve Surface Properties Sensitivity and Specificity Precision ab: Background: Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the tooth surface using these images. Methods: In this prospective study, 2348 in-house intraoral photographic images were collected from 445 participants using a professional intraoral camera at a dental clinic in a university medical centre from October 2020 to December 2021. Images were randomly assigned to training (1638), validation (410), and test (300) datasets. For image segmentation of the tooth surface, classification, and localisation of caries, convolutional neural networks (CNN), namely U-Net, ResNet-18, and Faster R-CNN, were applied. Results: For the classification algorithm for caries images, the accuracy and area under the receiver operating characteristic curve were improved to 0.813 and 0.837 from 0.758 to 0.731, respectively, through segmentation of the tooth surface using CNN. Localisation algorithm for carious lesions after segmentation of the tooth area also showed improved performance. For example, sensitivity and average precision improved from 0.890 to 0.889 to 0.865 and 0.868, respectively. Conclusion: The deep learning model with segmentation of the tooth surface is promising for caries detection on photographic images from an intraoral camera. This may be an aided diagnostic method for caries with the advantages of being time and cost-saving. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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