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Ariji Y, Kusano K, Fukuda M, Wakata Y, Nozawa M, Kotaki S, Ariji E, Baba S. Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs. Odontology 2024:10.1007/s10266-024-00989-z. [PMID: 39198339 DOI: 10.1007/s10266-024-00989-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 08/07/2024] [Indexed: 09/01/2024]
Abstract
The purpose of this study is to develop two-step deep learning models that can automatically detect implant regions on panoramic radiographs and identify several types of implants. A total of 1,574 panoramic radiographs containing 3675 implants were included. The implant manufacturers were Kyocera, Dentsply Sirona, Straumann, and Nobel Biocare. Model A was created to detect oral implants and identify the manufacturers using You Only Look Once (YOLO) v7. After preparing the image patches that cropped the implant regions detected by model A, model B was created to identify the implant types per manufacturer using EfficientNet. Model A achieved very high performance, with recall of 1.000, precision of 0.979, and F1 score of 0.989. It also had accuracy, recall, precision, and F1 score of 0.98 or higher for the classification of the manufacturers. Model B had high classification metrics above 0.92, exception for Nobel's class 2 (Parallel). In this study, two-step deep learning models were built to detect implant regions, identify four manufacturers, and identify implant types per manufacturer.
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Affiliation(s)
- Yoshiko Ariji
- Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan.
| | - Kaoru Kusano
- Department of Oral Implantology, Osaka Dental University, Osaka, Japan
| | - Motoki Fukuda
- Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan
| | - Yo Wakata
- Department of Oral Implantology, Osaka Dental University, Osaka, Japan
| | - Michihito Nozawa
- Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan
| | - Shinya Kotaki
- Department of Oral Radiology, Osaka Dental University, 1-5-17, Otemae, Chuo-ku, Osaka, 540-0008, Japan
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University, Nagoya, Japan
| | - Shunsuke Baba
- Department of Oral Implantology, Osaka Dental University, Osaka, Japan
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2
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Fuglsig JMDCES, Reis INRD, Yeung AWK, Bornstein MM, Spin-Neto R. The current role and future potential of digital diagnostic imaging in implant dentistry: A scoping review. Clin Oral Implants Res 2024; 35:793-809. [PMID: 37990981 DOI: 10.1111/clr.14212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/23/2023]
Abstract
OBJECTIVES Diagnostic imaging is crucial for implant dentistry. This review provides an up-to-date perspective on the application of digital diagnostic imaging in implant dentistry. METHODS Electronic searches were conducted in PubMed focusing on the question 'when (and why) do we need diagnostic imaging in implant dentistry?' The search results were summarised to identify different applications of digital diagnostic imaging in implant dentistry. RESULTS The most used imaging modalities in implant dentistry include intraoral periapical radiographs, panoramic views and cone beam computed tomography (CBCT). These are dependent on acquisition standardisation to optimise image quality. Particularly for CBCT, other technical parameters (i.e., tube current, tube voltage, field-of-view, voxel size) are relevant minimising the occurrence of artefacts. There is a growing interest in digital workflows, integrating diagnostic imaging and automation. Artificial intelligence (AI) has been incorporated into these workflows and is expected to play a significant role in the future of implant dentistry. Preliminary evidence supports the use of ionising-radiation-free imaging modalities (e.g., MRI and ultrasound) that can add value in terms of soft tissue visualisation. CONCLUSIONS Digital diagnostic imaging is the sine qua non in implant dentistry. Image acquisition protocols must be tailored to the patient's needs and clinical indication, considering the trade-off between radiation exposure and needed information. growing evidence supporting the benefits of digital workflows, from planning to execution, and the future of implant dentistry will likely involve a synergy between human expertise and AI-driven intelligence. Transiting into ionising-radiation-free imaging modalities is feasible, but these must be further developed before clinical implementation.
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Affiliation(s)
| | | | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, University of Hong Kong, Hong Kong, China
| | - Michael M Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Rubens Spin-Neto
- Section for Oral Radiology and Endodontics, Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark
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Macrì M, D’Albis V, D’Albis G, Forte M, Capodiferro S, Favia G, Alrashadah AO, García VDF, Festa F. The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review. Bioengineering (Basel) 2024; 11:778. [PMID: 39199736 PMCID: PMC11351972 DOI: 10.3390/bioengineering11080778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Revised: 07/24/2024] [Accepted: 07/26/2024] [Indexed: 09/01/2024] Open
Abstract
Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including "artificial intelligence implantology", "AI implant planning", "AI dental implant", and "implantology artificial intelligence". Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of AI in implant planning. The results of the literature review indicate a growing interest in the application of AI in implant planning, with evidence suggesting an improvement in precision and predictability compared to traditional methods. The summary of the obtained findings by the included studies represents the latest AI developments in implant planning, demonstrating its application for the automated detection of bones, the maxillary sinus, neuronal structure, and teeth. However, some disadvantages were also identified, including the need for high-quality training data and the lack of standardization in protocols. In conclusion, the use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management. However, further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.
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Affiliation(s)
- Monica Macrì
- Department of Innovative Technologies in Medicine & Dentistry, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy; (V.D.); (F.F.)
| | - Vincenzo D’Albis
- Department of Innovative Technologies in Medicine & Dentistry, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy; (V.D.); (F.F.)
| | - Giuseppe D’Albis
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | - Marta Forte
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | - Saverio Capodiferro
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | - Gianfranco Favia
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | | | - Victor Diaz-Flores García
- Department of Pre-Clinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, Villaviciosa de Odón, 28670 Madrid, Spain;
| | - Felice Festa
- Department of Innovative Technologies in Medicine & Dentistry, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy; (V.D.); (F.F.)
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Okawa J, Hori K, Izuno H, Fukuda M, Ujihashi T, Kodama S, Yoshimoto T, Sato R, Ono T. Developing tongue coating status assessment using image recognition with deep learning. J Prosthodont Res 2024; 68:425-431. [PMID: 37766551 DOI: 10.2186/jpr.jpr_d_23_00117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
PURPOSE To build an image recognition network to evaluate tongue coating status. METHODS Two image recognition networks were built: one for tongue detection and another for tongue coating classification. Digital tongue photographs were used to develop both networks; images from 251 (178 women, 74.7±6.6 years) and 144 older adults (83 women, 73.8±7.3 years) who volunteered to participate were used for the tongue detection network and coating classification network, respectively. The learning objective of the tongue detection network is to extract a rectangular region that includes the tongue. You-Only-Look-Once (YOLO) v2 was used as the detection network, and transfer learning was performed using ResNet-50. The accuracy was evaluated by calculating the intersection over the union. For tongue coating classification, the rectangular area including the tongue was divided into a grid of 7×7. Five experienced panelists scored the tongue coating in each area using one of five grades, and the tongue coating index (TCI) was calculated. Transfer learning for tongue coating grades was performed using ResNet-18, and the TCI was calculated. Agreement between the panelists and network for the tongue coating grades in each area and TCI was evaluated using the kappa coefficient and intraclass correlation, respectively. RESULTS The tongue detection network recognized the tongue with a high intersection over union (0.885±0.081). The tongue coating classification network showed high agreement with tongue coating grades and TCI, with a kappa coefficient of 0.826 and an intraclass correlation coefficient of 0.807, respectively. CONCLUSIONS Image recognition enables simple and detailed assessment of tongue coating status.
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Affiliation(s)
- Jumpei Okawa
- Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Kazuhiro Hori
- Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Hiromi Izuno
- Department of Oral Health Sciences, Faculty of Nursing and Health Care, BAIKA Women's University, Ibaraki, Japan
| | - Masayo Fukuda
- Department of Oral Health Science, Faculty of Health Science, Kobe Tokiwa University, Kobe, Japan
| | - Takako Ujihashi
- Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
- Department of Oral Health Science, Faculty of Health Science, Kobe Tokiwa University, Kobe, Japan
| | - Shohei Kodama
- Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Tasuku Yoshimoto
- Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Rikako Sato
- Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Takahiro Ono
- Division of Comprehensive Prosthodontics, Faculty of Dentistry & Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
- Department of Geriatric Dentistry, Osaka Dental University, Osaka, Japan
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5
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Ramachandran RA, Koseoglu M, Özdemir H, Bayindir F, Sukotjo C. Machine learning model to predict the width of maxillary central incisor from anthropological measurements. J Prosthodont Res 2024; 68:432-440. [PMID: 37853625 DOI: 10.2186/jpr.jpr_d_23_00114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
PURPOSE To improve smile esthetics, clinicians should comprehensively analyze the face and ensure that the sizes selected for the maxillary anterior teeth are compatible with the available anthropological measurements. The inter commissural (ICW), interalar (IAW), intermedial-canthus (MCW), interlateral-canthus (LCW), and interpupillary (IPW) widths are used to determine the width of maxillary central incisors (CW). The aim of this study was to develop an automated approach using machine learning (ML) algorithms to predict central incisor width in a young Turkish population using anthropological measurements. This automation can contribute to digital dentistry and clinical decision-making. METHODS In the initial phase of this cross-sectional study, several ML regression models-including multiple linear regression (MLR), multi-layer-perceptron (MLP), decision-tree (DT), and random forest (RF) models-were validated to confirm the central width prediction accuracy. Datasets containing only male and female measurements, as well as combined were considered for ML model implementation, and the performance of each model was evaluated for an unbiased population dataset. RESULTS Compared with the other algorithms, the RF algorithm showed improved performance for all cases, with an accuracy of 96%, which represents the percentage of correct predictions. The plot reveals the applicability of the RF model in predicting the CW from anthropological measurements irrespective of the candidate's sex. CONCLUSIONS These results demonstrated the possibility of predicting central incisor widths based on anthropometric measurements using ML models. The accurate central incisor width prediction from these trials also indicates the applicability of the proposed model to be deployed for enhanced clinical decision-making.
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Affiliation(s)
- Remya Ampadi Ramachandran
- 1DATA Consortium, Computational Comparative Medicine, Department of Mathematics, K-State Olathe, Olathe, USA
| | - Merve Koseoglu
- Department of Prosthodontics, Faculty of Dentistry, University of Sakarya, Serdivan, Turkey
| | - Hatice Özdemir
- Department of Prosthodontics, Faculty of Dentistry, University of Ataturk, Erzurum, Turkey
| | - Funda Bayindir
- Department of Prosthodontics, Faculty of Dentistry, University of Ataturk, Erzurum, Turkey
| | - Cortino Sukotjo
- Department of Restorative Dentistry, College of Dentistry, University of Illinois Chicago, Chicago, IL, USA
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Wang J, Wang B, Liu YY, Luo YL, Wu YY, Xiang L, Yang XM, Qu YL, Tian TR, Man Y. Recent Advances in Digital Technology in Implant Dentistry. J Dent Res 2024; 103:787-799. [PMID: 38822563 DOI: 10.1177/00220345241253794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2024] Open
Abstract
Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.
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Affiliation(s)
- J Wang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - B Wang
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Sichuan, Henan
| | - Y Y Liu
- Department of Oral Implantology, The Affiliated Stomatological Hospital of Kunming Medical University, Kunming, Yunnan, Sichuan, China
| | - Y L Luo
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Wu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - L Xiang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - X M Yang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y L Qu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - T R Tian
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Man
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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Hase H, Mine Y, Okazaki S, Yoshimi Y, Ito S, Peng TY, Sano M, Koizumi Y, Kakimoto N, Tanimoto K, Murayama T. Sex estimation from maxillofacial radiographs using a deep learning approach. Dent Mater J 2024; 43:394-399. [PMID: 38599831 DOI: 10.4012/dmj.2023-253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
The purpose of this study was to construct deep learning models for more efficient and reliable sex estimation. Two deep learning models, VGG16 and DenseNet-121, were used in this retrospective study. In total, 600 lateral cephalograms were analyzed. A saliency map was generated by gradient-weighted class activation mapping for each output. The two deep learning models achieved high values in each performance metric according to accuracy, sensitivity (recall), precision, F1 score, and areas under the receiver operating characteristic curve. Both models showed substantial differences in the positions indicated in saliency maps for male and female images. The positions in saliency maps also differed between VGG16 and DenseNet-121, regardless of sex. This analysis of our proposed system suggested that sex estimation from lateral cephalograms can be achieved with high accuracy using deep learning.
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Affiliation(s)
- Hiroki Hase
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuichi Mine
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
- Project Research Center for Integrating Digital Dentistry, Hiroshima University
| | - Shota Okazaki
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
- Project Research Center for Integrating Digital Dentistry, Hiroshima University
| | - Yuki Yoshimi
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Shota Ito
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Tzu-Yu Peng
- School of Dentistry, College of Oral Medicine, Taipei Medical University
| | - Mizuho Sano
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuma Koizumi
- Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Naoya Kakimoto
- School of Dentistry, College of Oral Medicine, Taipei Medical University
| | - Kotaro Tanimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Takeshi Murayama
- Department of Medical Systems Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
- Project Research Center for Integrating Digital Dentistry, Hiroshima University
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Revilla-León M, Gómez-Polo M, Sailer I, Kois JC, Rokhshad R. An overview of artificial intelligence based applications for assisting digital data acquisition and implant planning procedures. J ESTHET RESTOR DENT 2024. [PMID: 38757761 DOI: 10.1111/jerd.13249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVES To provide an overview of the current artificial intelligence (AI) based applications for assisting digital data acquisition and implant planning procedures. OVERVIEW A review of the main AI-based applications integrated into digital data acquisitions technologies (facial scanners (FS), intraoral scanners (IOSs), cone beam computed tomography (CBCT) devices, and jaw trackers) and computer-aided static implant planning programs are provided. CONCLUSIONS The main AI-based application integrated in some FS's programs involves the automatic alignment of facial and intraoral scans for virtual patient integration. The AI-based applications integrated into IOSs programs include scan cleaning, assist scanning, and automatic alignment between the implant scan body with its corresponding CAD object while scanning. The more frequently AI-based applications integrated into the programs of CBCT units involve positioning assistant, noise and artifacts reduction, structures identification and segmentation, airway analysis, and alignment of facial, intraoral, and CBCT scans. Some computer-aided static implant planning programs include patient's digital files, identification, labeling, and segmentation of anatomical structures, mandibular nerve tracing, automatic implant placement, and surgical implant guide design.
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Affiliation(s)
- Marta Revilla-León
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Research and Digital Dentistry, Kois Center, Seattle, Washington, USA
- Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA
| | - Miguel Gómez-Polo
- Department of Conservative Dentistry and Prosthodontics, Complutense University of Madrid, Madrid, Spain
- Advanced in Implant-Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - Irena Sailer
- Fixed Prosthodontics and Biomaterials, University Clinic of Dental Medicine, University of Geneva, Geneva, Switzerland
| | - John C Kois
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, University of Washington, Seattle, Washington, USA
- Private Practice, Seattle, Washington, USA
| | - Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
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Elgarba BM, Fontenele RC, Tarce M, Jacobs R. Artificial intelligence serving pre-surgical digital implant planning: A scoping review. J Dent 2024; 143:104862. [PMID: 38336018 DOI: 10.1016/j.jdent.2024.104862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
OBJECTIVES To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software. DATA AND SOURCES A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed. STUDY SELECTION AND RESULTS From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol. CONCLUSIONS AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated. CLINICAL SIGNIFICANCE Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.
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Affiliation(s)
- Bahaaeldeen M Elgarba
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Prosthodontics, Faculty of Dentistry, Tanta University, 31511 Tanta, Egypt.
| | - Rocharles Cavalcante Fontenele
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium
| | - Mihai Tarce
- Division of Periodontology & Implant Dentistry, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China & Periodontology and Oral Microbiology, Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
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10
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Zhao R, Xie R, Ren N, Li Z, Zhang S, Liu Y, Dong Y, Yin AA, Zhao Y, Bai S. Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study. Clin Oral Implants Res 2024; 35:258-267. [PMID: 38031528 DOI: 10.1111/clr.14222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 09/05/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVES This 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 METHODS A 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. RESULTS By 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. CONCLUSION The 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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Affiliation(s)
- Ruifeng Zhao
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
- Department of Stomatology, 960 Hospital of the Chinese People's Liberation Army, Jinan, Shandong, China
| | - Rui Xie
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
| | - Nan Ren
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
| | - Zhiwen Li
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
| | - Shengrui Zhang
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
| | - Yuchen Liu
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
| | - Yu Dong
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
- Department of Stomatology, Xi'an No.3 Hospital, the Affiliated Hospital of Northwest University, Xi'an, Shaanxi, China
| | - An-An Yin
- Department of Plastic and Reconstructive Surgery, Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Yimin Zhao
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
| | - Shizhu Bai
- Digital Center, School of Stomatology, The Fourth Military Medical University, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration & National Clinical Research Center for Oral Diseases & Shaanxi Key Laboratory of Stomatology, Xi'an, Shaanxi, China
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11
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Ying S, Huang F, Liu W, He F. Deep learning in the overall process of implant prosthodontics: A state-of-the-art review. Clin Implant Dent Relat Res 2024. [PMID: 38286659 DOI: 10.1111/cid.13307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/31/2024]
Abstract
Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.
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Affiliation(s)
- Shunv Ying
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Feng Huang
- School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Wei Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Fuming He
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
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12
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Altalhi AM, Alharbi FS, Alhodaithy MA, Almarshedy BS, Al-Saaib MY, Al Jfshar RM, Aljohani AS, Alshareef AH, Muhayya M, Al-Harbi NH. The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review. Cureus 2023; 15:e47941. [PMID: 38034167 PMCID: PMC10685062 DOI: 10.7759/cureus.47941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
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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Affiliation(s)
| | | | | | | | | | | | | | - Adeeb H Alshareef
- Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, SAU
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13
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Saleh O, Nozaki K, Matsumura M, Yanaka W, Abdou A, Miura H, Fueki K. Emergence angle: Comprehensive analysis and machine learning prediction for clinical application. J Prosthodont Res 2023; 67:468-474. [PMID: 36403962 DOI: 10.2186/jpr.jpr_d_22_00194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
PURPOSE To analyze and compare the emergence angle (EA) using two measurement methods, conventional and modified (EA-GPT and EA-R), the EAs of all-natural teeth were evaluated and classified to derive a suitable and predictable clinically applicable measurement method. METHODS Natural human teeth (n=600) were classified, cleaned, and thoroughly inspected. Teeth were scanned using an intraoral scanner. The scanned data were analyzed using three-dimensional analysis software for both methods with several points per surface. A Bland-Altman analysis was used for statistical analysis and a heat map and a nonparametric density plot to assess the repetition and distribution. An XGBoost regression model was used for prediction. RESULTS The EA-R method showed significantly different values compared to the EA-GPT method, representing an increase of 17.5-20.7% for the proximal surfaces. An insignificant difference between the two methods was observed for other surfaces. Different teeth classes showed variation in the normal range, thereby resulting in a new classification of the EA for all-natural teeth based on the interquartile range. The machine learning gradient boosting model predicted conventional data with an average mean absolute error of 0.9. CONCLUSIONS Variations in the natural teeth EA and measurement methods, suggest a new classification for EA. The established artificial intelligence method demonstrated robust performance, which could aid in implementing EA measurement in prosthetic designs.
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Affiliation(s)
- Omnia Saleh
- Department of Masticatory Function and Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kosuke Nozaki
- Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mayuko Matsumura
- Department of Masticatory Function and Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Wataru Yanaka
- Department of Masticatory Function and Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ahmed Abdou
- Department of Prosthodontics Dentistry, Faculty of Dentistry, King Salman International University, Cairo, Egypt
| | - Hiroyuki Miura
- Department of Masticatory Function and Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kenji Fueki
- Department of Masticatory Function and Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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14
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Yamaguchi S. JPR step forwards to new stage in 2023. J Prosthodont Res 2023; 67:viii-ix. [PMID: 36624064 DOI: 10.2186/jpr.jpr_d_22_00299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Satoshi Yamaguchi
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Suita, Osaka 565-0871, Japan
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