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Șalgău CA, Morar A, Zgarta AD, Ancuța DL, Rădulescu A, Mitrea IL, Tănase AO. Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review. Ann Biomed Eng 2024:10.1007/s10439-024-03559-0. [PMID: 38884831 DOI: 10.1007/s10439-024-03559-0] [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: 04/03/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024]
Abstract
Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.
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Affiliation(s)
- Cristiana Adina Șalgău
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Anca Morar
- National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.
| | | | - Diana-Larisa Ancuța
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
- Cantacuzino National Medical-Military Institute for Research and Development, Bucharest, Romania
| | - Alexandros Rădulescu
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Ioan Liviu Mitrea
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Andrei Ovidiu Tănase
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
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Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review. Bioengineering (Basel) 2024; 11:431. [PMID: 38790300 PMCID: PMC11118054 DOI: 10.3390/bioengineering11050431] [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: 03/12/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.
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Affiliation(s)
| | - Shayan Darvish
- School of Dentistry, University of Michigan, Ann Arbor, MI 48104, USA;
| | - Bahareh Nazemi Salman
- Department of Pediatric Dentistry, School of Dentistry, Zanjan University of Medical Sciences, Zanjan 4513956184, Iran
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
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Ibraheem WI. Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Diagnostics (Basel) 2024; 14:806. [PMID: 38667452 PMCID: PMC11049199 DOI: 10.3390/diagnostics14080806] [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: 02/24/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was "What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.
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Affiliation(s)
- Wael I Ibraheem
- Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
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Delamare E, Fu X, Huang Z, Kim J. Panoramic imaging errors in machine learning model development: a systematic review. Dentomaxillofac Radiol 2024; 53:165-172. [PMID: 38273661 PMCID: PMC11003661 DOI: 10.1093/dmfr/twae002] [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: 09/07/2023] [Revised: 12/11/2023] [Accepted: 01/01/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models. METHODS This systematic literature followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and used three databases. Keywords were selected from relevant literature. ELIGIBILITY CRITERIA PAN studies that used ML models and mentioned image quality concerns. RESULTS Out of 400 articles, 41 papers satisfied the inclusion criteria. All the studies used ML models, with 35 papers using deep learning (DL) models. PAN quality assessment was approached in 3 ways: acknowledgement and acceptance of imaging errors in the ML model, removal of low-quality radiographs from the dataset before building the model, and application of image enhancement methods prior to model development. The criteria for determining PAN image quality varied widely across studies and were prone to bias. CONCLUSIONS This study revealed significant inconsistencies in the management of PAN imaging errors in ML research. However, most studies agree that such errors are detrimental when building ML models. More research is needed to understand the impact of low-quality inputs on model performance. Prospective studies may streamline image quality assessment by leveraging DL models, which excel at pattern recognition tasks.
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Affiliation(s)
- Eduardo Delamare
- Sydney Dental School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2050, Australia
- Digital Health and Data Science, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, 2050, Australia
| | - Xingyue Fu
- School of Computer Science, Faculty of Engineering, The University of Sydney, Camperdown, NSW, 2050, Australia
| | - Zimo Huang
- School of Computer Science, Faculty of Engineering, The University of Sydney, Camperdown, NSW, 2050, Australia
| | - Jinman Kim
- School of Computer Science, Faculty of Engineering, The University of Sydney, Camperdown, NSW, 2050, Australia
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Umer F, Adnan S, Lal A. Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review. BMC Oral Health 2024; 24:220. [PMID: 38347508 PMCID: PMC10860267 DOI: 10.1186/s12903-024-03970-y] [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: 09/21/2023] [Accepted: 02/02/2024] [Indexed: 02/15/2024] Open
Abstract
Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.
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Affiliation(s)
- Fahad Umer
- Department of Surgery, Section of Dentistry, The Aga Khan University, Karachi, Pakistan
| | - Samira Adnan
- Department of Operative Dentistry, Sindh Institute of Oral Health Sciences, Jinnah Sindh Medical University, Karachi, Pakistan
| | - Abhishek Lal
- Department of Medicine, Section of Gastroenterology, The Aga Khan University, Karachi, Pakistan.
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Dashti M, Londono J, Ghasemi S, Tabatabaei S, Hashemi S, Baghaei K, Palma PJ, Khurshid Z. Evaluation of accuracy of deep learning and conventional neural network algorithms in detection of dental implant type using intraoral radiographic images: A systematic review and meta-analysis. J Prosthet Dent 2024:S0022-3913(23)00812-0. [PMID: 38176985 DOI: 10.1016/j.prosdent.2023.11.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
STATEMENT OF PROBLEM With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising results for the use of machine learning in implant brand detection. However, despite these promising findings, a comprehensive evaluation of the accuracy of machine learning in implant brand detection is needed. PURPOSE The purpose of this systematic review and meta-analysis was to assess the accuracy, sensitivity, and specificity of deep learning algorithms in implant brand detection using 2-dimensional images such as from periapical or panoramic radiographs. MATERIAL AND METHODS Electronic searches were conducted in PubMed, Embase, Scopus, Scopus Secondary, and Web of Science databases. Studies that met the inclusion criteria were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analyses were performed using the random-effects model to estimate the pooled performance measures and the 95% confidence intervals (CIs) using STATA v.17. RESULTS Thirteen studies were selected for the systematic review, and 3 were used in the meta-analysis. The meta-analysis of the studies found that the overall accuracy of CNN algorithms in detecting dental implants in radiographic images was 95.63%, with a sensitivity of 94.55% and a specificity of 97.91%. The highest reported accuracy was 99.08% for CNN Multitask ResNet152 algorithm, and sensitivity and specificity were 100.00% and 98.70% respectively for the deep CNN (Neuro-T version 2.0.1) algorithm with the Straumann SLActive BLT implant brand. All studies had a low risk of bias. CONCLUSIONS The highest accuracy and sensitivity were reported in studies using CNN Multitask ResNet152 and deep CNN (Neuro-T version 2.0.1) algorithms.
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Affiliation(s)
- Mahmood Dashti
- Researcher, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Jimmy Londono
- Professor and Director of the Prosthodontics Residency Program and the Ronald Goldstein Center for Esthetics and Implant Dentistry, The Dental College of Georgia at Augusta University, Augusta, GA
| | - Shohreh Ghasemi
- Graduate Student, MSc of Trauma and Craniofacial Reconstrution, Faculty of Medicine and Dentistry, Queen Mary College, London, England
| | | | - Sara Hashemi
- Graduate student, Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Kimia Baghaei
- Researcher, Dental Students' Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Paulo J Palma
- Researcher, Center for Innovation and Research in Oral Sciences (CIROS), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; and Professor, Institute of Endodontics, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
| | - Zohaib Khurshid
- Lecturer, Prosthodontics, Department of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa, Saudi Arabia; and Professor, Center of Excellence for Regenerative Dentistry, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
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Alqutaibi AY, Algabri RS, Elawady D, Ibrahim WI. Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis. J Prosthet Dent 2023:S0022-3913(23)00783-7. [PMID: 38158266 DOI: 10.1016/j.prosdent.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024]
Abstract
STATEMENT OF PROBLEM The evidence regarding the application of artificial intelligence (AI) in identifying dental implant systems is currently inconclusive. The available studies present varying results and methodologies, making it difficult to draw definitive conclusions. PURPOSE The purpose of this systematic review with meta-analysis was to comprehensively analyze and evaluate articles that investigate the application of AI in identifying and classifying dental implant systems. MATERIAL AND METHODS An electronic systematic review was conducted across 3 databases: MEDLINE/PubMed, Cochrane, and Scopus. Additionally, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies investigating the accuracy of AI-based diagnostic tools on dental radiographs for identifying and classifying dental implant systems and comparing the results with those obtained by expert judges using manual techniques-the search strategy encompassed articles published until September 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of included articles. RESULTS Twenty-two eligible articles were included in this review. These articles described the use of AI in detecting dental implants through conventional radiographs. The pooled data showed that dental implant identification had an overall accuracy of 92.56% (range 90.49% to 94.63%). Eleven studies showed a low risk of bias, 6 demonstrated some concern risk, and 5 showed a high risk of bias. CONCLUSIONS AI models using panoramic and periapical radiographs can accurately identify and categorize dental implant systems. However, additional well-conducted research is recommended to identify the most common implant systems.
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Affiliation(s)
- Ahmed Yaseen Alqutaibi
- Associate Professor, Department of Prosthodontics and Implant Dentistry, College of Dentistry, Taibah University, Al Madinah, Saudi Arabia; and Associate Professor, Department of Prosthodontics, College of Dentistry, Ibb University, Ibb, Yemen.
| | - Radhwan S Algabri
- Assistant professor, Department of Prosthodontics, Faculty of Dentistry, Ibb University, Ibb, Yemen; and Assistant professor, Department of Prosthodontics, Faculty of Dentistry, National University, Ibb, Yemen
| | - Dina Elawady
- Associate Professor, Department of Prosthodontics, Faculty of Dentistry, MSA University, 6th of October City, Egypt
| | - Wafaa Ibrahim Ibrahim
- Associate Professor, Department of Prosthodontics, Faculty of Oral and Dental Medicine, Delta University for Science and Technology, Mansoura, Egypt
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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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Tabatabaian F, Vora SR, Mirabbasi S. Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review. J ESTHET RESTOR DENT 2023; 35:842-859. [PMID: 37522291 DOI: 10.1111/jerd.13079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination. OVERVIEW An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed. CONCLUSIONS Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice. CLINICAL SIGNIFICANCE AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.
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Affiliation(s)
- Farhad Tabatabaian
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Siddharth R Vora
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shahriar Mirabbasi
- Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, British Columbia, Canada
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Bornes RS, Montero J, Correia ARM, Rosa NRDN. Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review. J Prosthet Dent 2023; 129:322.e1-322.e8. [PMID: 36710172 DOI: 10.1016/j.prosdent.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/29/2022] [Accepted: 12/29/2022] [Indexed: 01/29/2023]
Abstract
STATEMENT OF PROBLEM The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants. PURPOSE The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success. MATERIAL AND METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question "How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?" RESULTS Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement. CONCLUSIONS The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies-biomarkers-are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.
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Affiliation(s)
- Rita Silva Bornes
- Guest Lecturer, Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
| | - Javier Montero
- Full professor in Prosthodontics, Department of Surgery, Faculty of Medicine, University of Salamanca, Salamanca, Spain
| | - André Ricardo Maia Correia
- Assistant Professor, Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal
| | - Nuno Ricardo das Neves Rosa
- Assistant Professor, Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal
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Kurt M, Kurt Z, Işık Ş. Using deep learning approaches for coloring silicone maxillofacial prostheses: A comparison of two approaches. J Indian Prosthodont Soc 2023; 23:84-89. [PMID: 36588380 PMCID: PMC10088445 DOI: 10.4103/jips.jips_149_22] [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: 03/25/2022] [Revised: 06/14/2022] [Accepted: 06/30/2022] [Indexed: 12/31/2022] Open
Abstract
Aim This study aimed to compare the performance of two deep learning algorithms, attention-based gated recurrent unit (GRU), and the artificial neural networks (ANNs) algorithm for coloring silicone maxillofacial prostheses. Settings and Design This was an in vitro study. Materials and Methods A total of 21 silicone samples in different colors were produced with four pigments (white, yellow, red, and blue). The color of the samples was measured with a spectrophotometer, then the LFNx01, aFNx01, and bFNx01 values were recorded. The relationship between the LFNx01, aFNx01, and bFNx01 values of each sample and the amount of each pigment in the compound of the same sample was used as the training dataset, entered into each algorithm, and the prediction models were obtained. While generating the prediction model for each sample, the data of the corresponding sample assigned as the target color were excluded. LFNx01, aFNx01, and bFNx01 values of each target sample were entered into the obtained models separately, and recipes indicating the ratios for mixing the four pigments were predicted. The mean absolute error (MAE) and root mean square error (RMSE) values between the original recipe used in the production of each silicone and the recipe created by both prediction models for the same silicone were calculated. Statistical Analysis Used Data were analyzed with the Student t-test (α=0.05). Results The mean RMSE values and MAE values for the ANN algorithm (0.029 ± 0.0152 and 0.045 ± 0.0235, respectively) were found significantly higher than the attention-based GRU model (0.001 ± 0.0005 and 0.002 ± 0.0008, respectively) (P < 0.001). Conclusions Attention-based GRU model provided better performance than the ANN algorithm with respect to the MAE and RMSE values.
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Affiliation(s)
- Meral Kurt
- Department of Prosthodontics, Faculty of Dentistry, Gazi University, Ankara, Turkey
| | - Zuhal Kurt
- Department of Computer Engineering, Faculty of Engineering, Atilim University, Ankara, Turkey
| | - Şahin Işık
- Department of Computer Engineering, Faculty of Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey
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Kohlakala A, Coetzer J, Bertels J, Vandermeulen D. Deep learning-based dental implant recognition using synthetic X-ray images. Med Biol Eng Comput 2022; 60:2951-2968. [PMID: 35978215 PMCID: PMC9385426 DOI: 10.1007/s11517-022-02642-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022]
Abstract
Abstract A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. Graphical abstract ![]()
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Affiliation(s)
- Aviwe Kohlakala
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Johannes Coetzer
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Jeroen Bertels
- ESAT, Centre for Processing Speech and Images, KU Leuven, Leuven, Belgium
| | - Dirk Vandermeulen
- ESAT, Centre for Processing Speech and Images, KU Leuven, Leuven, Belgium
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