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Cho JH, Çakmak G, Choi J, Lee D, Yoon HI, Yilmaz B, Schimmel M. Deep learning-designed implant-supported posterior crowns: Assessing time efficiency, tooth morphology, emergence profile, occlusion, and proximal contacts. J Dent 2024; 147:105142. [PMID: 38906454 DOI: 10.1016/j.jdent.2024.105142] [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: 02/21/2024] [Revised: 06/10/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024] Open
Abstract
OBJECTIVES To compare implant supported crowns (ISCs) designed using deep learning (DL) software with those designed by a technician using conventional computer-aided design software. METHODS Twenty resin-based partially edentulous casts (maxillary and mandibular) used for fabricating ISCs were evaluated retrospectively. ISCs were designed using a DL-based method with no modification of the as-generated outcome (DB), a DL-based method with further optimization by a dental technician (DM), and a conventional computer-aided design method by a technician (NC). Time efficiency, crown contour, occlusal table area, cusp angle, cusp height, emergence profile angle, occlusal contacts, and proximal contacts were compared among groups. Depending on the distribution of measured data, various statistical methods were used for comparative analyses with a significance level of 0.05. RESULTS ISCs in the DB group showed a significantly higher efficiency than those in the DM and NC groups (P ≤ 0.001). ISCs in the DM group exhibited significantly smaller volume deviations than those in the DB group when superimposed on ISCs in the NC group (DB-NC vs. DM-NC pairs, P ≤ 0.008). Except for the number and intensity of occlusal contacts (P ≤ 0.004), ISCs in the DB and DM groups had occlusal table areas, cusp angles, cusp heights, proximal contact intensities, and emergence profile angles similar to those in the NC group (P ≥ 0.157). CONCLUSIONS A DL-based method can be beneficial for designing posterior ISCs in terms of time efficiency, occlusal table area, cusp angle, cusp height, proximal contact, and emergence profile, similar to the conventional human-based method. CLINICAL SIGNIFICANCE A deep learning-based design method can achieve clinically acceptable functional properties of posterior ISCs. However, further optimization by a technician could improve specific outcomes, such as the crown contour or emergence profile angle.
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Affiliation(s)
- Jun-Ho Cho
- Department of Prosthodontics, Seoul National University Dental Hospital, Seoul, Republic of Korea
| | - Gülce Çakmak
- Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland
| | - Jinhyeok Choi
- Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Dongwook Lee
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Hyung-In Yoon
- Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland; Department of Prosthodontics, School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea.
| | - Burak Yilmaz
- Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland; Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland; Division of Restorative and Prosthetic Dentistry, The Ohio State University, Columbus, OH, United States
| | - Martin Schimmel
- Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland; Division of Gerodontology and Removable Prosthodontics, University Clinics of Dental Medicine, University of Geneva, Geneva, Switzerland
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Ali IE, Tanikawa C, Chikai M, Ino S, Sumita Y, Wakabayashi N. Applications and performance of artificial intelligence models in removable prosthodontics: A literature review. J Prosthodont Res 2024; 68:358-367. [PMID: 37793819 DOI: 10.2186/jpr.jpr_d_23_00073] [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: 10/06/2023]
Abstract
PURPOSE In this narrative review, we present the current applications and performances of artificial intelligence (AI) models in different phases of the removable prosthodontic workflow and related research topics. STUDY SELECTION A literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar databases between January 2010 and January 2023. Search terms related to AI were combined with terms related to removable prosthodontics. Articles reporting the structure and performance of the developed AI model were selected for this literature review. RESULTS A total of 15 articles were relevant to the application of AI in removable prosthodontics, including maxillofacial prosthetics. These applications included the design of removable partial dentures, classification of partially edentulous arches, functional evaluation and outcome prediction in complete denture treatment, early prosthetic management of patients with cleft lip and palate, coloration of maxillofacial prostheses, and prediction of the material properties of denture teeth. Various AI models with reliable prediction accuracy have been developed using supervised learning. CONCLUSIONS The current applications of AI in removable prosthodontics exhibit significant potential for improving the prosthodontic workflow, with high accuracy levels reported in most of the reviewed studies. However, the focus has been predominantly on the diagnostic phase, with few studies addressing treatment planning and implementation. Because the number of AI-related studies in removable prosthodontics is limited, more models targeting different prosthodontic disciplines are required.
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Affiliation(s)
- Islam E Ali
- Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Prosthodontics, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Chihiro Tanikawa
- Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Osaka University, Suita, Japan
| | - Manabu Chikai
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Shuichi Ino
- Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, Suita, Japan
| | - Yuka Sumita
- Department of Partial and Complete Denture, School of Life Dentistry at Tokyo, The Nippon Dental University, Tokyo, Japan
- Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Noriyuki Wakabayashi
- Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Aljulayfi IS, Almatrafi AH, Althubaitiy RO, Alnafisah F, Alshehri K, Alzahrani B, Gufran K. The Potential of Artificial Intelligence in Prosthodontics: A Comprehensive Review. Med Sci Monit 2024; 30:e944310. [PMID: 38840416 PMCID: PMC11178143 DOI: 10.12659/msm.944310] [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: 02/28/2024] [Accepted: 04/09/2024] [Indexed: 06/07/2024] Open
Abstract
Prosthodontics is a dental subspecialty that includes the preparation of dental prosthetics for missing or damaged teeth. It increasingly uses computer-assisted technologies for planning and preparing dental prosthetics. This study aims to present the findings from a systematic review of publications on artificial intelligence (AI) in prosthodontics to identify current trends and future opportunities. The review question was "What are the applications of AI in prosthodontics and how good is their performance in prosthodontics?" Electronic searching in the Web of Science, ScienceDirect, PubMed, and Cochrane Library was conducted. The search was limited to full text from January 2012 to January 2024. Quadas-2 was used for assessing quality and potential risk of bias for the selected studies. A total of 1925 studies were identified in the initial search. After removing the duplicates and applying exclusion criteria, a total of 30 studies were selected for this review. Results of the Quadas-2 assessment of included studies found that a total of 18.3% of studies were identified as low risk of bias studies, whereas 52.6% and 28.9% of included studies were identified as studies with high and unclear risk of bias, respectively. Although they are still developing, AI models have already shown promise in the areas of dental charting, tooth shade selection, automated restoration design, mapping the preparation finishing line, manufacturing casting optimization, predicting facial changes in patients wearing removable prostheses, and designing removable partial dentures.
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Affiliation(s)
- Ibrahim Saleh Aljulayfi
- Department of Prosthetic Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | | | - Ramzi O. Althubaitiy
- Department of Prosthetic Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Fahad Alnafisah
- Dental Intern, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Khalid Alshehri
- Dental Intern, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Bandar Alzahrani
- Dental Intern, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Khalid Gufran
- Department of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Chau RCW, Hsung RTC, McGrath C, Pow EHN, Lam WYH. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J Prosthet Dent 2024; 131:1111-1117. [PMID: 36631366 DOI: 10.1016/j.prosdent.2022.12.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023]
Abstract
STATEMENT OF PROBLEM Computer-aided design and computer-aided manufacturing (CAD-CAM) technology has greatly improved the efficiency of the fabrication of dental prostheses. However, the design process (CAD stage) is still time-consuming and labor intensive. PURPOSE The purpose of this feasibility study was to investigate the accuracy of a novel artificial intelligence (AI) system in designing biomimetic single-molar dental prostheses by comparing and matching them to the natural molar teeth. MATERIAL AND METHODS A total of 169 maxillary casts were obtained from healthy dentate participants. The casts were digitized, duplicated, and processed with the removal of the maxillary right first molar. A total of 159 pairs of original and processed casts were input into the Generative Adversarial Networks (GANs) for training. In validation, 10 sets of processed casts were input into the AI system, and 10 AI-designed teeth were generated through backpropagation. Individual AI-designed teeth were then superimposed onto each of the 10 original teeth, and the morphological differences in mean Hausdorff distance were measured. True reconstruction was defined as correct matching between the AI-designed and original teeth with the smallest mean Hausdorff distance. The ratio of true reconstruction was calculated as the Intersection-over-Union. The reconstruction performance of the AI system was determined by the Hausdorff distance and Intersection-over-Union. RESULTS Data of validation showed that the mean Hausdorff distance ranged from 0.441 to 0.752 mm and the Intersection-over-Union of the system was 0.600 (60%). CONCLUSIONS This study demonstrated the feasibility of AI in designing single-molar dental prostheses. With further training and optimization of algorithms, the accuracy of biomimetic AI-designed dental prostheses could be further enhanced.
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Affiliation(s)
- Reinhard Chun Wang Chau
- Research Assistant, Restorative Dental Sciences, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region, PR China
| | - Richard Tai-Chiu Hsung
- Associate Professor, Department of Computer Science, Chu Hai College of Higher Education, Hong Kong Special Administrative Region, PR China; Honorary Associate Professor, Discipline of Oral and Maxillofacial Surgery, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region, PR China
| | - Colman McGrath
- Clinical Professor in Dental Public Health and Division Coordinator of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region, PR China
| | - Edmond Ho Nang Pow
- Clinical Associate Professor in Prosthodontics, Restorative Dental Sciences, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region, PR China
| | - Walter Yu Hang Lam
- Clinical Assistant Professor in Prosthodontics, Restorative Dental Sciences, Faculty of Dentistry, the University of Hong Kong, Hong Kong Special Administrative Region, PR China; Founding Member, Musketeers Foundation Institute of Data Science, the University of Hong Kong, Hong Kong Special Administrative Region, PR China.
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Aktaş N, Bani M, Ocak M, Bankoğlu Güngör M. Effects of design software program and manufacturing method on the marginal and internal adaptation of esthetic crowns for primary teeth: A microcomputed tomography evaluation. J Prosthet Dent 2024; 131:519.e1-519.e9. [PMID: 38195256 DOI: 10.1016/j.prosdent.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/10/2023] [Accepted: 12/12/2023] [Indexed: 01/11/2024]
Abstract
STATEMENT OF PROBLEM The adaptation of digitally produced crowns is affected by the design software program and manufacturing method. The effect of artificial intelligence (AI) software program design on the adaptation of the crowns is unclear and comparative evaluations should be documented. PURPOSE The purpose of this study was to assess the marginal and internal gaps, the absolute marginal discrepancies, and the 3-dimensional (3D) discrepancy volumes of the resin-based milled and 3D printed crowns for primary teeth designed with computer-aided design (CAD) and AI software programs by using microcomputed tomography (µCT). MATERIAL AND METHODS A total of 40 resin-based esthetic crowns were produced for a prepared typodont tooth (right mandibular primary second molar) according to the design software program (CAD and AI) and manufacturing method (milling and 3D printing) (n=10). Four experimental groups were generated as CAD-milled, CAD-3D printed, AI-milled, and AI-3D printed. The marginal, axial, and occlusal gap values, the absolute marginal discrepancies, and the 3D discrepancy volumes of the specimens were measured by using µCT. The data were analyzed by using 2-way ANOVA and the Tukey HSD tests (α=.05). RESULTS The lowest value for the marginal gap (54 ±43 µm) was observed in the CAD-milled group and the marginal gap value of the AI-3D printed group was significantly lower than the AI-milled group (P<.05). The lowest value for the axial gap (63 ±7 µm) was observed in the AI-3D printed group, and the highest value (145 ±58 µm) was observed in the CAD-milled group; the result for the occlusal gap value was opposite. The highest absolute marginal discrepancy value was observed in the CAD-milled group. The 3D discrepancy volumes increased in the order of the CAD-3D printed, AI-milled, CAD-milled, and AI-3D printed groups. CONCLUSIONS The marginal and internal gap values of the resin-based crowns were affected by the design software program and manufacturing method; however, tested groups showed clinically acceptable gap values.
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Affiliation(s)
- Nagehan Aktaş
- Assistant Professor, Department of Paediatric Dentistry, Faculty of Dentistry, Gazi University, Ankara, Turkey
| | - Mehmet Bani
- Professor, Department of Paediatric Dentistry, Faculty of Dentistry, Gazi University, Ankara, Turkey
| | - Mert Ocak
- Assistant Professor, Department of Anatomy, Faculty of Dentistry, Ankara University, Ankara, Turkey
| | - Merve Bankoğlu Güngör
- Professor, Department of Prosthodontics, Faculty of Dentistry, Gazi University, Ankara, Turkey.
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Zhang JS, Huang S, Chen Z, Chu CH, Takahashi N, Yu OY. Application of omics technologies in cariology research: A critical review with bibliometric analysis. J Dent 2024; 141:104801. [PMID: 38097035 DOI: 10.1016/j.jdent.2023.104801] [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: 09/19/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/19/2023] Open
Abstract
OBJECTIVES To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges. DATA & SOURCES Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review. STUDY SELECTION Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %). CONCLUSION This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research. CLINICAL SIGNIFICANCE This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.
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Affiliation(s)
| | - Shi Huang
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China
| | - Zigui Chen
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China; Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, PR China
| | - Chun-Hung Chu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China
| | - Nobuhiro Takahashi
- Division of Oral Ecology and Biochemistry, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Ollie Yiru Yu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China.
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Heboyan A, Yazdanie N, Ahmed N. Glimpse into the future of prosthodontics: The synergy of artificial intelligence. World J Clin Cases 2023; 11:7940-7942. [DOI: 10.12998/wjcc.v11.i33.7940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/26/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Prosthodontics, deals in the restoration and replacement of missing and structurally compromised teeth, this field has been remarkably transformed in the last two decades. Through the integration of digital imaging and three-dimensional printing, prosthodontics has evolved to provide more durable, precise, and patient-centric outcome. However, as we stand at the convergence of technology and healthcare, a new era is emerging, one that holds immense promise for the field and that is artificial intelligence (AI). In this paper, we explored the fascinating challenges and prospects associated with the future of prosthodontics in the era of AI.
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Affiliation(s)
- Artak Heboyan
- Department of Prosthodontics, Yerevan State Medical University after Mkhitar Heratsi, Yerevan 0025, Armenia
| | - Nazia Yazdanie
- Department of Prosthodontics, FMH College of Medicine and Dentistry, Lahore 54000, Pakistan
| | - Naseer Ahmed
- Department of Prosthodontics, Altammash Institute of Dental Medicine, Karachi 75500, Pakistan
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Chau RCW, Li GH, Tew IM, Thu KM, McGrath C, Lo WL, Ling WK, Hsung RTC, Lam WYH. Accuracy of Artificial Intelligence-Based Photographic Detection of Gingivitis. Int Dent J 2023; 73:724-730. [PMID: 37117096 PMCID: PMC10509417 DOI: 10.1016/j.identj.2023.03.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/19/2023] [Accepted: 03/20/2023] [Indexed: 04/30/2023] Open
Abstract
OBJECTIVES Gingivitis is one of the most prevalent plaque-initiated dental diseases globally. It is challenging to maintain satisfactory plaque control without continuous professional advice. Artificial intelligence may be used to provide automated visual plaque control advice based on intraoral photographs. METHODS Frontal view intraoral photographs fulfilling selection criteria were collected. Along the gingival margin, the gingival conditions of individual sites were labelled as healthy, diseased, or questionable. Photographs were randomly assigned as training or validation datasets. Training datasets were input into a novel artificial intelligence system and its accuracy in detection of gingivitis including sensitivity, specificity, and mean intersection-over-union were analysed using validation dataset. The accuracy was reported according to STARD-2015 statement. RESULTS A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50. CONCLUSIONS Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients' plaque control.
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Affiliation(s)
- Reinhard Chun Wang Chau
- Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Guan-Hua Li
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - In Meei Tew
- Faculty of Dentistry, The National University of Malaysia, Kuala Lumpur, Malaysia
| | - Khaing Myat Thu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Colman McGrath
- Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Wai-Lun Lo
- Department of Computer Science, Hong Kong Chu Hai College, Hong Kong Special Administrative Region, China
| | - Wing-Kuen Ling
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Richard Tai-Chiu Hsung
- Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China; School of Information Engineering, Guangdong University of Technology, Guangzhou, China; Department of Computer Science, Hong Kong Chu Hai College, Hong Kong Special Administrative Region, China.
| | - Walter Yu Hang Lam
- Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region, China; Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong Special Administrative Region, China.
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Texture-Based Neural Network Model for Biometric Dental Applications. J Pers Med 2022; 12:jpm12121954. [PMID: 36556175 PMCID: PMC9781388 DOI: 10.3390/jpm12121954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/23/2022] [Accepted: 11/23/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. METHODS Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intraoral scanner and identified using a texture-based CNN in three steps. First, through preprocessing, teeth images were segmented by extracting the front-facing region of the teeth. Then, texture features were extracted from the segmented teeth images using the discrete wavelet transform (DWT) method. Finally, deep learning-based enhanced CNN models were used to identify these images. Several experiments were conducted using five different CNN models with various batch sizes and epochs, with and without augmented data. RESULTS Based on experiments with five different CNN models, the highest accuracy achieved was 0.8 and the precision was 0.8 with a loss value of 0.9, a batch size of 32, and 250 epochs. A comparison of deep learning models with different parameters showed varied accuracy between the different classes of teeth. CONCLUSION The accuracy of the point-based CNN method was promising. This texture-identification method will pave the way for many forensic and prosthodontic applications and will potentially help improve the precision of dental biometrics.
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Sakai T, Li H, Shimada T, Kita S, Iida M, Lee C, Nakano T, Yamaguchi S, Imazato S. Development of artificial intelligence model for supporting implant drilling protocol decision making. J Prosthodont Res 2022. [PMID: 36002334 DOI: 10.2186/jpr.jpr_d_22_00053] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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. CONCLUSION 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.
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Affiliation(s)
- Takahiko Sakai
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan.,Department of Fixed Prosthodontics, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Hefei Li
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Tatsuki Shimada
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Suzune Kita
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Maho Iida
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Chunwoo Lee
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Tamaki Nakano
- Department of Fixed Prosthodontics, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Satoshi Yamaguchi
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan
| | - Satoshi Imazato
- Department of Biomaterials Science, Osaka University Graduate School of Dentistry, Osaka, Japan
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