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A deep learning approach to automatic tooth detection and numbering in panoramic radiographs: An artificial intelligence study

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dc.contributor.authorKESER, GAYE
dc.contributor.authorNAMDAR PEKİNER, FİLİZ MEDİHA
dc.contributor.authorsMERTOĞLU D., KESER G., BAYRAKDAR İ. Ş., NAMDAR PEKİNER F. M., ÇELİK Ö., ORHAN K.
dc.date.accessioned2023-12-27T06:40:17Z
dc.date.available2023-12-27T06:40:17Z
dc.date.issued2023-12-01
dc.description.abstractObjective: n this study, in order to test the usability of artificial intelligence technologies in dentistry, which are becoming widespread and expanding day by day, and to investigate ways to benefit more from artificial intelligence technologies; a tooth detection and numbering study was performed on panoramic radiographs using a deep learning software. Methods: A radiographic dataset containing 200 anonymous panoramic radiographs collected from individuals over the age of 18 was assessed in this retrospective investigation. The images were separated into three groups: training (80%), validation (10%), and test (10%), and tooth numbering was performed with the DCNN artificial intelligence software. Results: The D-CNN system has been successful in detecting and numbering teeth. of teeth. The predicted precision, sensitivity, and F1 score were 0.996 (98.0%), 0.980 (98.0%), and 0.988 (98.8%), respectively. Conclusion: The precision, sensitivity and F1 scores obtained in our study were found to be high, as 0.996 (98.0%), 0.980 (98.0%) and 0.988 (98.8%), respectively. Although the current algorithm based on Faster R-CNN shows promising results, future studies should be done by increasing the number of data for better tooth detection and numbering results.
dc.identifier.citationMERTOĞLU D., KESER G., BAYRAKDAR İ. Ş., NAMDAR PEKİNER F. M., ÇELİK Ö., ORHAN K., "A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study", CLINICAL AND EXPERIMENTAL HEALTH SCIENCES, cilt.13, sa.4, ss.883-888, 2023
dc.identifier.doi10.33808/clinexphealthsci.1219160
dc.identifier.endpage888
dc.identifier.issn2459-1459
dc.identifier.issue4
dc.identifier.startpage883
dc.identifier.urihttp://dx.doi.org/10.33808/clinexphealthsci.1219160
dc.identifier.urihttps://hdl.handle.net/11424/296057
dc.identifier.volume13
dc.language.isoeng
dc.relation.ispartofCLINICAL AND EXPERIMENTAL HEALTH SCIENCES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial intelligence
dc.subjectdeep learning
dc.subjectpanoramic radiography
dc.subjecttooth numbering
dc.titleA deep learning approach to automatic tooth detection and numbering in panoramic radiographs: An artificial intelligence study
dc.typearticle
dspace.entity.typePublication
local.avesis.id1545c350-c5e8-4e0c-a88d-c15c7696923c
relation.isAuthorOfPublication3045e390-5443-4605-a33a-40beae74e9c4
relation.isAuthorOfPublication17385174-2d0f-4945-b843-afe511f63f03
relation.isAuthorOfPublication.latestForDiscovery3045e390-5443-4605-a33a-40beae74e9c4

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