The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study

Introduction: 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 denta...

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Autores principales:	Pathompong Roongruangsilp, Pathawee Khongkhunthian
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence deep learning dental implant Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/796974f6163b4e8bad70d99ed6516a9d
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spelling	oai:doaj.org-article:796974f6163b4e8bad70d99ed6516a9d2021-11-11T15:13:19ZThe Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study10.3390/app1121101592076-3417https://doaj.org/article/796974f6163b4e8bad70d99ed6516a9d2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10159https://doaj.org/toc/2076-3417Introduction: 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., Bangkok, 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. Conclusions: 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.Pathompong RoongruangsilpPathawee KhongkhunthianMDPI AGarticleartificial intelligencedeep learningdental implantTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10159, p 10159 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence deep learning dental implant Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	artificial intelligence deep learning dental implant Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Pathompong Roongruangsilp Pathawee Khongkhunthian The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study
description	Introduction: 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., Bangkok, 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. Conclusions: 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.
format	article
author	Pathompong Roongruangsilp Pathawee Khongkhunthian
author_facet	Pathompong Roongruangsilp Pathawee Khongkhunthian
author_sort	Pathompong Roongruangsilp
title	The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study
title_short	The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study
title_full	The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study
title_fullStr	The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study
title_full_unstemmed	The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study
title_sort	learning curve of artificial intelligence for dental implant treatment planning: a descriptive study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/796974f6163b4e8bad70d99ed6516a9d
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The Learning Curve of Artificial Intelligence for Dental Implant Treatment Planning: A Descriptive Study

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