ログイン
言語:

WEKO3

  • トップ
  • ランキング
To
lat lon distance
To

Field does not validate



インデックスリンク

インデックスツリー

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

  1. 資料タイプ別
  2. 学術雑誌論文

Application of artificial intelligence in the dental field : A literature review

https://tokushima-u.repo.nii.ac.jp/records/2009985
https://tokushima-u.repo.nii.ac.jp/records/2009985
9edf830c-99bc-4412-bd22-2412dbd1670c
名前 / ファイル ライセンス アクション
jpor_66_1_19.pdf jpor_66_1_19.pdf (742 KB)
license.icon
Item type 文献 / Documents(1)
公開日 2022-06-15
アクセス権
アクセス権 open access
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
出版社版DOI
関連識別子 https://doi.org/10.2186/jpr.JPR_D_20_00139
関連名称 10.2186/jpr.JPR_D_20_00139
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
タイトル
タイトル Application of artificial intelligence in the dental field : A literature review
著者 岸本, 卓大

× 岸本, 卓大

WEKO 1163
徳島大学 教育研究者総覧 364643/profile-ja.html

ja 岸本, 卓大
ISNI

ja-Kana キシモト, タカヒロ

en Kishimoto, Takahiro

Search repository
後藤, 崇晴

× 後藤, 崇晴

WEKO 570
徳島大学 教育研究者総覧 246762/profile-ja.html
e-Rad 00581381

ja 後藤, 崇晴
ISNI

ja-Kana ゴトウ, タカハル

en Goto, Takaharu

Search repository
松田, 岳

× 松田, 岳

WEKO 830
徳島大学 教育研究者総覧 310659/profile-ja.html

ja 松田, 岳
ISNI

ja-Kana マツダ, タカシ

en Matsuda, Takashi

Search repository
岩脇, 有軌

× 岩脇, 有軌

WEKO 835
徳島大学 教育研究者総覧 312289/profile-ja.html
e-Rad 10754624

ja 岩脇, 有軌
ISNI

ja-Kana イワワキ, ユウキ

en Iwawaki, Yuki

Search repository
市川, 哲雄

× 市川, 哲雄

WEKO 1652
徳島大学 教育研究者総覧 60312/profile-ja.html
e-Rad 90193432

ja 市川, 哲雄
ISNI

ja-Kana イチカワ, テツオ

en Ichikawa, Tetsuo

Search repository
抄録
内容記述 Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types.
Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included.
Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve.
Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning.
キーワード
主題 Artificial intelligence
キーワード
主題 Data mining
キーワード
主題 Machine learning
キーワード
主題 Neural Networks
キーワード
主題 Dental field
書誌情報 en : Journal of Prosthodontic Research

巻 66, 号 1, p. 19-28, 発行日 2021-01-14
収録物ID
収録物識別子タイプ ISSN
収録物識別子 18839207
収録物ID
収録物識別子タイプ ISSN
収録物識別子 18831958
収録物ID
収録物識別子タイプ NCID
収録物識別子 AA12395171
出版者
出版者 Japan Prosthodontic Society
権利情報
権利情報 This is an open-access article distributed under the terms of Creative Commons Attribution-NonCommercial License 4.0 (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/deed.en), which allows users to distribute and copy the material in any format as long as credit is given to the Japan Prosthodontic Society. It should be noted however, that the material cannot be used for commercial purposes.
EID
識別子 378617
言語
言語 eng
戻る
0
views
See details
Views

Versions

Ver.1 2024-12-12 07:33:14.489678
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR 2.0
  • OAI-PMH JPCOAR 1.0
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3