Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/75987
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dc.contributor.authorPathompong Roongruangsilpen_US
dc.contributor.authorPathawee Khongkhunthianen_US
dc.date.accessioned2022-10-16T07:04:04Z-
dc.date.available2022-10-16T07:04:04Z-
dc.date.issued2021-11-01en_US
dc.identifier.issn20763417en_US
dc.identifier.other2-s2.0-85118102295en_US
dc.identifier.other10.3390/app112110159en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85118102295&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/75987-
dc.description.abstractIntroduction: Cone‐beam computed tomography (CBCT) has been applied to implant dentistry. The increasing use of this technology produces a critical number of images that can be used for training artificial intelligence (AI). Objectives: To investigate the learning curve of the developed AI for dental implant planning in the posterior maxillary region. Methods: A total of 184 CBCT image sets of patients receiving posterior maxillary implants were processed with software (DentiPlan Pro version 3.7; NECTEC, NSTDA, Thailand) to acquire 316 implant position images. The planning software image interfaces were anonymously captured with full‐screen resolution. Three hundred images were randomly sorted to create six data sets, including 1–50, 1–100, 1–150, 1–200, 1–250, and 1–300. The data sets were used to develop AI for dental implant planning through the IBM PowerAI Vision platform (IBM Thailand Co., Ltd., Thailand) by using a faster R‐CNN algorithm. Four data augmentation algorithms, including blur, sharpen, color, and noise, were also integrated to observe the improvement of the model. After the testing process with 16 images that were not included in the training set, the recorded data were analyzed for detection and accuracy to generate the learning curve of the model. Results: The learning curve revealed some similar patterns. The curve trend of the original and blurred augmented models was in a similar pattern in the panoramic image. In the last training set, the blurred augmented model improved the detection by 12.50%, but showed less accuracy than the original model by 18.34%, whereas the other three augmented models had different patterns. They were continuously increasing in both detection and accuracy. However, their detection dropped in the last training set. The colored augmented model demonstrated the best improvement with 40% for the panoramic image and 18.59% for the cross-sectional image. Conclusion: Within the limitation of the study, it may be concluded that the number of images used in AI development is positively related to the AI interpretation. The data augmentation techniques to improve the ability of AI are still questionable.en_US
dc.subjectChemical Engineeringen_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectMaterials Scienceen_US
dc.subjectPhysics and Astronomyen_US
dc.titleThe learning curve of artificial intelligence for dental implant treatment planning: A descriptive studyen_US
dc.typeJournalen_US
article.title.sourcetitleApplied Sciences (Switzerland)en_US
article.volume11en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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