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

Sakai, Taiji; Li, Hefei; Shimada, Tatsuki; Kita, Suzune; Iida, Maho; Lee, Chunwoo; Nakano, Tamaki; Imazato, Satoshi

doi:10.2186/jpr.jpr_d_22_00053

Cited by 21 publications

(17 citation statements)

References 36 publications

(38 reference statements)

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“…However, the apparent collinearity disabled the application of conventional multiple linear regression for temperature prediction. In recent years, ML, an important subfield of artificial intelligence (AI), has been regarded as a robust tool to handle high‐dimensional data with interdependence and cross‐correlation (Grischke et al., 2020; Mohammad‐Rahimi et al., 2022; Sakai et al., 2022; Zhang et al., 2021). ML can automatically learn from input data, continuously self‐improve its performance, and theoretically provide optimal data interrogation (Li et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

Zhao,

Xie,

Ren

et al. 2023

Clinical Oral Implants Res

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ObjectivesThis study aims at examining the correlation of intraosseous temperature change with drilling impulse data during osteotomy and establishing real‐time temperature prediction models.Materials and MethodsA combination of in vitro bovine rib model and Autonomous Dental Implant Robotic System (ADIR) was set up, in which intraosseous temperature and drilling impulse data were measured using an infrared camera and a six‐axis force/torque sensor respectively. A total of 800 drills with different parameters (e.g., drill diameter, drill wear, drilling speed, and thickness of cortical bone) were experimented, along with an independent test set of 200 drills. Pearson correlation analysis was done for linear relationship. Four machining learning (ML) algorithms (e.g., support vector regression [SVR], ridge regression [RR], extreme gradient boosting [XGboost], and artificial neural network [ANN]) were run for building prediction models.ResultsBy incorporating different parameters, it was found that lower drilling speed, smaller drill diameter, more severe wear, and thicker cortical bone were associated with higher intraosseous temperature changes and longer time exposure and were accompanied with alterations in drilling impulse data. Pearson correlation analysis further identified highly linear correlation between drilling impulse data and thermal changes. Finally, four ML prediction models were established, among which XGboost model showed the best performance with the minimum error measurements in test set.ConclusionThe proof‐of‐concept study highlighted close correlation of drilling impulse data with intraosseous temperature change during osteotomy. The ML prediction models may inspire future improvement on prevention of thermal bone injury and intelligent design of robot‐assisted implant surgery.

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Section: Discussionmentioning

confidence: 99%

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

Zhao,

Xie,

Ren

et al. 2023

Clinical Oral Implants Res

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show abstract

“…They believed that their novel protocol was efficient and time-saving for simple cases of guided implant surgery. An interesting study was performed by Sakai et al where they used pre-op CBCT scans to predict implant drilling protocols [ 17 ]. Three drilling protocols were analyzed - conventional drilling protocol with a tapping drill, conventional drilling protocol without a tapping drill, and undersized drilling protocol.…”

Section: Reviewmentioning

confidence: 99%

The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review

Altalhi,

Alharbi,

Alhodaithy

et al. 2023

Cureus

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Implant dentistry has witnessed a transformative shift with the integration of artificial intelligence (AI) technologies. This article explores the role of AI in implant dentistry, emphasizing its impact on diagnostics, treatment planning, and patient outcomes. AI-driven image analysis and deep learning algorithms enhance the precision of implant placement, reducing risks and optimizing aesthetics. Moreover, AI-driven data analytics provide valuable insights into patient-specific treatment strategies, improving overall success rates. As AI continues to evolve, it promises to reshape the landscape of implant dentistry and lead in an era of personalized and efficient oral healthcare.

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“…Deep learning (DL) has been widely used and accepted in a variety of artificial intelligence (AI) systems in medical imaging analysis, ranging from disease prevention, diagnosis, and prognosis in pathology, dermatology, ophthalmology, cardiology, and so on (Esteva et al 2021). Likewise, recent DL-related research in the dental field, including dental radiology, cariology, periodontology, orthodontics, and dental implantology, has shown preliminary value in clinical application and a reliable accuracy performance, comparable to that of dental professionals (Pauwels 2021; Mohammad-Rahimi et al 2022; Sakai et al 2022). Several studies have suggested that DL as a decision support aid may significantly improve the identification and classification accuracy of DISs based on dental periapical and panoramic radiographic images (Lee and Jeong 2020; Lee, Kim, Lee, Jeong 2020; Lee et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Identification of Dental Implant Systems Using a Large-Scale Multicenter Data Set

et al. 2023

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This study aimed to evaluate the efficacy of deep learning (DL) for the identification and classification of various types of dental implant systems (DISs) using a large-scale multicenter data set. We also compared the classification accuracy of DL and dental professionals. The data set, which was collected from 5 college dental hospitals and 10 private dental clinics, contained 37,442 (24.8%) periapical and 113,291 (75.2%) panoramic radiographic images and consisted of a total of 10 manufacturers and 25 different types of DISs. The classification accuracy of DL was evaluated using a pretrained and modified ResNet-50 architecture, and comparison of accuracy performance and reading time between DL and dental professionals was conducted using a self-reported questionnaire. When comparing the accuracy performance for classification of DISs, DL (accuracy: 82.0%; 95% confidence interval [CI], 75.9%–87.0%) outperformed most of the participants (mean accuracy: 23.5% ± 18.5%; 95% CI, 18.5%–32.3%), including dentists specialized (mean accuracy: 43.3% ± 20.4%; 95% CI, 12.7%–56.2%) and not specialized (mean accuracy: 16.8% ± 9.0%; 95% CI, 12.8%–20.9%) in implantology. In addition, DL tends to require lesser reading and classification time (4.5 min) than dentists who specialized (75.6 ± 31.0 min; 95% CI, 13.1–78.4) and did not specialize (91.3 ± 38.3 min; 95% CI, 74.1–108.6) in implantology. DL achieved reliable outcomes in the identification and classification of various types of DISs, and the classification accuracy performance of DL was significantly superior to that of specialized or nonspecialized dental professionals. DL as a decision support aid can be successfully used for the identification and classification of DISs encountered in clinical practice.

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

Abstract: Artificial intelligence (AI) can predict an output from unknown input data by pre-learning the relationship between two known datasets. Deep learning is a type of machine learning approach and is J Prosthodont Res. 2022; ():

Cited by 21 publications

References 36 publications

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review

Identification of Dental Implant Systems Using a Large-Scale Multicenter Data Set

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

Abstract: Artificial intelligence (AI) can predict an output from unknown input data by pre-learning the relationship between two known datasets. Deep learning is a type of machine learning approach and is J Prosthodont Res. 2022; **(**):

Cited by 21 publications

References 36 publications

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review

Identification of Dental Implant Systems Using a Large-Scale Multicenter Data Set

Contact Info

Product

Resources

About

Abstract: Artificial intelligence (AI) can predict an output from unknown input data by pre-learning the relationship between two known datasets. Deep learning is a type of machine learning approach and is J Prosthodont Res. 2022; ():