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Ourang SA, Sohrabniya F, Mohammad-Rahimi H, Dianat O, Aminoshariae A, Nagendrababu V, Dummer PMH, Duncan HF, Nosrat A. Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks. Int Endod J 2024. [PMID: 39056554 DOI: 10.1111/iej.14127] [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: 05/21/2024] [Revised: 06/25/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024]
Abstract
The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.
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Affiliation(s)
- Seyed AmirHossein Ourang
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Sohrabniya
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- Private Practice, Irvine Endodontics, Irvine, California, USA
| | - Anita Aminoshariae
- Department of Endodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | | | | | - Henry F Duncan
- Division of Restorative Dentistry, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, Maryland, USA
- Private Practice, Centreville Endodontics, Centreville, Virginia, USA
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Real AD, Real OD, Sardina S, Oyonarte R. Use of automated artificial intelligence to predict the need for orthodontic extractions. Korean J Orthod 2022; 52:102-111. [PMID: 35321949 PMCID: PMC8964473 DOI: 10.4041/kjod.2022.52.2.102] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 11/10/2022] Open
Abstract
Objective To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.
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Affiliation(s)
- Alberto Del Real
- Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes, Santiago, Chile.,Private Practice, Santiago, Chile
| | - Octavio Del Real
- Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes, Santiago, Chile.,Private Practice, Santiago, Chile
| | - Sebastian Sardina
- Department of Computer Science, School of Computing Technologies, RMIT University, Melbourne, Australia
| | - Rodrigo Oyonarte
- Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes, Santiago, Chile.,Private Practice, Santiago, Chile
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Asiri SN, Tadlock LP, Schneiderman E, Buschang PH. Applications of artificial intelligence and machine learning in orthodontics. APOS TRENDS IN ORTHODONTICS 2020. [DOI: 10.25259/apos_117_2019] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.
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Affiliation(s)
- Saeed N. Asiri
- Departments of Orthodontics, College of Dentistry, Texas A&M University, Dallas, Texas, United States
- Departments of Biomedical Sciences, College of Dentistry, Texas A&M University, Dallas, Texas, United States,
| | - Larry P. Tadlock
- Departments of Orthodontics, College of Dentistry, Texas A&M University, Dallas, Texas, United States
| | - Emet Schneiderman
- Department of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia,
| | - Peter H. Buschang
- Departments of Orthodontics, College of Dentistry, Texas A&M University, Dallas, Texas, United States
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Thanathornwong B. Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment. Healthc Inform Res 2018; 24:22-28. [PMID: 29503749 PMCID: PMC5820082 DOI: 10.4258/hir.2018.24.1.22] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Revised: 01/14/2018] [Accepted: 01/18/2018] [Indexed: 11/23/2022] Open
Abstract
Objectives In this study, a clinical decision support system was developed to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition. Methods We chose a Bayesian network (BN) as the underlying model for assessing the need for orthodontic treatment. One thousand permanent dentition patient data sets chosen from a hospital record system were prepared in which one data element represented one participant with information for all variables and their stated need for orthodontic treatment. To evaluate the system, we compared the assessment results based on the judgements of two orthodontists to those recommended by the decision support system. Results In a BN decision support model, each variable is modelled as a node, and the causal relationship between two variables may be represented as a directed arc. For each node, a conditional probability table is supplied that represents the probabilities of each value of this node, given the conditions of its parents. There was a high degree of agreement between the two orthodontists (kappa value = 0.894) in their diagnoses and their judgements regarding the need for orthodontic treatment. Also, there was a high degree of agreement between the decision support system and orthodontists A (kappa value = 1.00) and B (kappa value = 0.894). Conclusions The study was the first testing phase in which the results generated by the proposed system were compared with those suggested by expert orthodontists. The system delivered promising results; it showed a high degree of accuracy in classifying patients into groups needing and not needing orthodontic treatment.
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Affiliation(s)
- Bhornsawan Thanathornwong
- Department of General Dentistry, Faculty of Dentistry, Srinakharinwirot University, Bangkok, Thailand
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Uncertain Decision-Making in Primary Root Canal Treatment. J Evid Based Dent Pract 2017; 17:205-215. [PMID: 28865817 DOI: 10.1016/j.jebdp.2017.01.001] [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: 10/21/2016] [Revised: 01/15/2017] [Accepted: 01/16/2017] [Indexed: 11/24/2022]
Abstract
OBJECTIVES A systematic review of literature was conducted to compare the success and survivability of primary root canal interventions. METHODS The Preferred Reporting Items for Systematic Reviews and Meta Analyses protocol was adopted in this study to systematically assess and report systematic reviews related to success or survival or failure rates of primary root canal interventions. MEDLINE and Cochrane Oral Health Library were both searched by using specific search terms to identify relevant literature, until June 2016. The search was augmented by handsearching. Then, the quality of the included systematic reviews was assessed by using the Revised Assessment of Multiple Systematic Reviews (RAMSTAR) protocol. RESULTS Only 9 systematic reviews were identified. The RAMSTAR scores of the included reviews ranged from 43/44 to 29/44. Nevertheless, the later reviews did not provide sufficient evidence or statistically significant evidence to support any of the interventions used during primary root canal treatment. In addition, a number of key steps during primary root canal treatment, such as types of dental files, root canal instrumentation techniques, orthograde obturation materials, and techniques, were not assessed by systematic reviews. CONCLUSION The current status of evidence related to the success and survivability of primary root canal interventions is lacking. This puts dentists under marked degrees of uncertainty. Consequently, patients are potentially exposed to health care risks. It is then essential to develop tailored methods and tools for decision-making under uncertainty to aid both dentists and patients engaged in primary root canal treatment.
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Thanathornwong B, Suebnukarn S, Ouivirach K. Decision support system for predicting color change after tooth whitening. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 125:88-93. [PMID: 26657921 DOI: 10.1016/j.cmpb.2015.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2015] [Revised: 10/14/2015] [Accepted: 11/03/2015] [Indexed: 06/05/2023]
Abstract
Tooth whitening is becoming increasingly popular among patients and dentists since it is a relatively noninvasive approach. However, the degree of color change after tooth whitening is known to vary substantially between studies. The present study aims to develop a clinical decision support system for predicting color change after in-office tooth whitening. We used the information from patients' data sets, and applied the multiple regression equation of CIELAB color coordinates including L*, a*, and b* of the original tooth color and the color difference (ΔE) that expresses the color change after tooth whitening. To evaluate the system performance, the patient's post-treatment color was used as "gold standard" to compare with the post-treatment color predicted by the system. There was a high degree of agreement between the patient's post-treatment color and the post-treatment color predicted by the system (kappa value=0.894). The results obtained have demonstrated that the decision support system is possible to predict the color change obtained using an in-office whitening system using colorimetric values.
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Thanathornwong B, Suebnukarn S, Songpaisan Y, Ouivirach K. A system for predicting and preventing work-related musculoskeletal disorders among dentists. Comput Methods Biomech Biomed Engin 2012; 17:177-85. [PMID: 22519570 DOI: 10.1080/10255842.2012.672565] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Work-related musculoskeletal disorders (WMSDs) have become increasingly common among dentists and initiate a series of events that could result in a career ending. This study aims to construct a system for predicting and preventing WMSD among dentists. We used Bayesian network (BN) that describes the mutual relationships among multiple variables contributing to WMSDs. The data-sets were prepared from direct measurements of dentist's movements and a questionnaire survey. We applied BN learning algorithms to the training data-sets to develop WMSD prediction model using 10-fold cross-validation. To evaluate the system performance, 16 dentists were randomly assigned into a 2 × 2 crossover trial scheduled to each of two sequences of dental working: receiving feedback or no feedback including the probability of WMSD and related risk factors from the system. The group that received feedback decreased significantly (t-test, p < 0.05) the extensions of neck and upper back in the y-axis as well as the WMSD probability on the post-test. In conclusion, the system for predicting and preventing WMSD aids the correction of neck and upper back extensions and reduction in WMSD probability, which may potentially contribute to reduce the risk of WMSD among dentists.
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Panitvisai P, Parunnit P, Sathorn C, Messer HH. Impact of a Retained Instrument on Treatment Outcome: A Systematic Review and Meta-analysis. J Endod 2010; 36:775-80. [DOI: 10.1016/j.joen.2009.12.029] [Citation(s) in RCA: 96] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2009] [Revised: 12/25/2009] [Accepted: 12/28/2009] [Indexed: 11/27/2022]
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Parashos P, Moore J, Fitz-Walter P. Response. Int Endod J 2010. [DOI: 10.1111/j.1365-2591.2010.01731.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Resende LM, Rached-Junior FJA, Versiani MA, Souza-Gabriel AE, Miranda CES, Silva-Sousa YTC, Sousa Neto MD. A comparative study of physicochemical properties of AH Plus, Epiphany, and Epiphany SE root canal sealers. Int Endod J 2009; 42:785-93. [DOI: 10.1111/j.1365-2591.2009.01584.x] [Citation(s) in RCA: 86] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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