Development of artificial intelligence model for supporting implant drilling protocol decision making

Sakai, Takahiko; Li, Hefei; Shimada, Tatsuki et al.

Journal of Prosthodontic Research, 2023, 67(3), 360-365

アクセス数:60件（2025-04-22 11:19 集計）

固定URL: https://hdl.handle.net/11094/93098

閲覧可能ファイル 

ファイル フォーマット 利用条件 サイズ 閲覧回数 利用開始日 説明 information
JProsthodonticRes_67_3_360 pdf なし 1.78 MB 91 2023.11.10  
Creative Commons : 表示 - 非営利

論文情報

ファイル出力 EndNote Basic出力 Mendeley出力

タイトル
Development of artificial intelligence model for supporting implant drilling protocol decision making
著者
著者名
Sakai, Takahiko
著者名
Li, Hefei
著者名
Shimada, Tatsuki
著者名
Kita, Suzune
著者名
Iida, Maho
著者名
Lee, Chunwoo
NRID
1000040379079 1000040379079
researchmap permalink
nkn nkn
Scopus Author ID
7403895984 7403895984
著者名
Nakano, Tamaki
NRID
1000030397773 1000030397773
ORCID
0000-0002-4661-8821 0000-0002-4661-8821
researchmap permalink
YamaguchiS_30397773 YamaguchiS_30397773
Scopus Author ID
7403873463 7403873463
著者名
Yamaguchi, Satoshi
NRID
1000080243244 1000080243244
ORCID
0000-0003-3655-4809 0000-0003-3655-4809
researchmap permalink
read0014210 read0014210
Scopus Author ID
55994423600 55994423600
著者名
Imazato, Satoshi
キーワード等
Artificial intelligence
Computed tomography
Deep learning/machine learning
Dental implant(s)
Prosth-odontic dentistry/prosthodontics
内容
Sakai T., Li H., Shimada T., et al. Development of artificial intelligence model for supporting implant drilling protocol decision making. Journal of Prosthodontic Research 67, 360 (2023); https://doi.org/10.2186/jpr.JPR_D_22_00053.
抄録
Purpose: This study aimed to develop an artificial intelligence (AI) model to support the determination of an appropriate implant drilling protocol using cone-beam computed tomography (CBCT) images. Methods: Anonymized CBCT images were obtained from 60 patients. For each case, after implant placement, images of the bone regions at the implant site were extracted from 20 slices of CBCT images. Based on the actual drilling protocol, the images were classified into three categories: protocols A, B, and C. A total of 1,200 images were divided into training and validation datasets (n = 960, 80%) and a test dataset (n = 240, 20%). Another 240 images (80 images for each type) were extracted from the 60 cases as test data. An AI model based on LeNet-5 was developed using these data sets. The accuracy, sensitivity, precision, F-value, area under the curve (AUC) value, and receiver operating curve were calculated. Results: The accuracy of the trained model is 93.8%. The sensitivity results for drilling protocols A, B, and C were 97.5%, 95.0%, and 85.0%, respectively, while those for protocols A, B, and C were 86.7%, 92.7%, and 100%, respectively, and the F values for protocols A, B, and C were 91.8%, 93.8%, and 91.9%, respectively. The AUC values for protocols A, B, and C are 98.6%, 98.6%, and 99.4%, respectively. Conclusions: The AI model established in this study was effective in predicting drilling protocols from CBCT images before surgery, suggesting the possibility of developing a decision-making support system to promote primary stability.
出版者
Japan Prosthodontic Society
収録物名
Journal of Prosthodontic Research
巻(号)
67(3)
ページ
360-365
刊行年月
2023-01-01
言語
英語
ハンドルURL
https://hdl.handle.net/11094/93098
PISSN
18831958
関連情報 (isIdenticalTo)
DOI (出版社版)
https://doi.org/10.2186/jpr.JPR_D_22_00053
アクセス権
open access
権利情報
© 2023, Japan Prosthodontic Society. All rights reserved.
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
出版タイプ
Version of Record
カテゴリ
学術雑誌論文 Journal Article