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

Number of Access:429（2025-08-14 15:06 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93098

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Title
Development of artificial intelligence model for supporting implant drilling protocol decision making
Creator
CreatorName
Sakai, Takahiko
CreatorName
Li, Hefei
CreatorName
Shimada, Tatsuki
CreatorName
Kita, Suzune
CreatorName
Iida, Maho
CreatorName
Lee, Chunwoo
NRID
1000040379079 1000040379079
researchmap permalink
nkn nkn
Scopus Author ID
7403895984 7403895984
CreatorName
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
CreatorName
Yamaguchi, Satoshi
NRID
1000080243244 1000080243244
ORCID
0000-0003-3655-4809 0000-0003-3655-4809
researchmap permalink
read0014210 read0014210
Scopus Author ID
55994423600 55994423600
CreatorName
Imazato, Satoshi
Subject
Artificial intelligence
Computed tomography
Deep learning/machine learning
Dental implant(s)
Prosth-odontic dentistry/prosthodontics
Description
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.
Abstract
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.
Publisher
Japan Prosthodontic Society
Source Title
Journal of Prosthodontic Research
Volume (Issue)
67(3)
Page
360-365
Date of Issued
2023-01-01
Language
English
Handle URL
https://hdl.handle.net/11094/93098
PISSN
18831958
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.2186/jpr.JPR_D_22_00053
Access Rights
open access
CopyRight
© 2023, Japan Prosthodontic Society. All rights reserved.
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article