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Asgary S. Artificial Intelligence in Endodontics: A Scoping Review. IRANIAN ENDODONTIC JOURNAL 2024; 19:85-98. [PMID: 38577001 PMCID: PMC10988643 DOI: 10.22037/iej.v19i2.44842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/23/2024] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Artificial intelligence (AI) is transforming the diagnostic methods and treatment approaches in the constantly evolving field of endodontics. The current review discusses the recent advancements in AI; with a specific focus on convolutional and artificial neural networks. Apparently, AI models have proved to be highly beneficial in the analysis of root canal anatomy, detecting periapical lesions in early stages as well as providing accurate working-length determination. Moreover, they seem to be effective in predicting the treatment success next to identifying various conditions e.g., dental caries, pulpal inflammation, vertical root fractures, and expression of second opinions for non-surgical root canal treatments. Furthermore, AI has demonstrated an exceptional ability to recognize landmarks and lesions in cone-beam computed tomography scans with consistently high precision rates. While AI has significantly promoted the accuracy and efficiency of endodontic procedures, it is of high importance to continue validating the reliability and practicality of AI for possible widespread integration into daily clinical practice. Additionally, ethical considerations related to patient privacy, data security, and potential bias should be carefully examined to ensure the ethical and responsible implementation of AI in endodontics.
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Affiliation(s)
- Saeed Asgary
- Iranian Center for Endodontic Research, Research Institute for Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Ahmed ZH, Almuharib AM, Abdulkarim AA, Alhassoon AH, Alanazi AF, Alhaqbani MA, Alshalawi MS, Almuqayrin AK, Almahmoud MI. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023; 24:912-917. [PMID: 38238281 DOI: 10.5005/jp-journals-10024-3593] [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: 01/23/2024]
Abstract
AIM AND BACKGROUND Artificial intelligence (AI) since it was introduced into dentistry, has become an important and valuable tool in many fields. It was applied in different specialties with different uses, for example, in diagnosis of oral cancer, periodontal disease and dental caries, and in the treatment planning and predicting the outcome of orthognathic surgeries. The aim of this comprehensive review is to report on the application and performance of AI models designed for application in the field of endodontics. MATERIALS AND METHODS PubMed, Web of Science, and Google Scholar were searched to collect the most relevant articles using terms, such as AI, endodontics, and dentistry. This review included 56 papers related to AI and its application in endodontics. RESULT The applications of AI were in detecting and diagnosing periapical lesions, assessing root fractures, working length determination, prediction for postoperative pain, studying root canal anatomy and decision-making in endodontics for retreatment. The accuracy of AI in performing these tasks can reach up to 90%. CONCLUSION Artificial intelligence has valuable applications in the field of modern endodontics with promising results. Larger and multicenter data sets can give external validity to the AI models. CLINICAL SIGNIFICANCE In the field of dentistry, AI models are specifically crafted to contribute to the diagnosis of oral diseases, ranging from common issues such as dental caries to more complex conditions like periodontal diseases and oral cancer. AI models can help in diagnosis, treatment planning, and in patient management in endodontics. Along with the modern tools like cone-beam computed tomography (CBCT), AI can be a valuable aid to the clinician. How to cite this article: Ahmed ZH, Almuharib AM, Abdulkarim AA, et al. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023;24(11):912-917.
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Affiliation(s)
- Zeeshan Heera Ahmed
- Department of Restorative Dental Sciences and Endodontics, College of Dentistry, King Saud University, Riyadh, Saudi Arabia, Phone: +966502318766, e-mail:
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Gomez-Rios I, Egea-Lopez E, Ortiz Ruiz AJ. ORIENTATE: automated machine learning classifiers for oral health prediction and research. BMC Oral Health 2023; 23:408. [PMID: 37340367 DOI: 10.1186/s12903-023-03112-w] [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: 03/08/2023] [Accepted: 06/06/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND The application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is preventing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific technical skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross-validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/or complement classical statistical studies. RESULTS Its application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided. CONCLUSIONS ORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation.
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Affiliation(s)
- Inmaculada Gomez-Rios
- Department of Dermatology, Stomatology, Radiology and Physical Medicine, Universidad de Murcia, Murcia, Spain
| | - Esteban Egea-Lopez
- Dept. Information Technologies and Communications, Universidad Politecnica de Cartagena (UPCT), Cartagena, Spain.
| | - Antonio José Ortiz Ruiz
- Department of Dermatology, Stomatology, Radiology and Physical Medicine, Universidad de Murcia, Murcia, Spain
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Evaluation of the Diagnostic and Prognostic Accuracy of Artificial Intelligence in Endodontic Dentistry: A Comprehensive Review of Literature. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2023; 2023:7049360. [PMID: 36761829 PMCID: PMC9904932 DOI: 10.1155/2023/7049360] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 10/23/2022] [Accepted: 11/26/2022] [Indexed: 02/01/2023]
Abstract
Aim This comprehensive review is aimed at evaluating the diagnostic and prognostic accuracy of artificial intelligence in endodontic dentistry. Introduction Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry. Results The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures. Conclusion In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.
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Agrawal P, Nikhade P. Artificial Intelligence in Dentistry: Past, Present, and Future. Cureus 2022; 14:e27405. [PMID: 36046326 PMCID: PMC9418762 DOI: 10.7759/cureus.27405] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 07/28/2022] [Indexed: 11/11/2022] Open
Abstract
Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision-making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.
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Association between patient-, tooth- and treatment-level factors and root canal treatment failure: A retrospective longitudinal and machine learning study. J Dent 2021; 117:103937. [PMID: 34942278 DOI: 10.1016/j.jdent.2021.103937] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/10/2021] [Accepted: 12/17/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES We aimed to assess patient-, tooth- and treatment-level covariates on the failure of root canal treatments (RT) and to predict failure using machine learning (ML). METHODS Teeth receiving RT at one large university hospital from 2016-2020 with a minimum follow-up of ≥6 months were included. Failure compromised absent radiographic healing and/or the presence of clinical symptoms. Covariates were selected on tooth-, treatment- and patient-level. We used logistic regression (logR) to determine associations in the full dataset, and logR as well as more advanced ML (random forests (RF), gradient boosting machine (GBM) and extremely gradient boosting (XGB)) for predictive modeling (area-under-the-receiver operating characteristic-curve (ROCAUC)) and testing on a separate test dataset. RESULTS 458 patients (female/male 47.2/52.8%) with 591 permanent teeth were included (overall success rate 79.5%). In logR, tooth-level covariates showed strong associations with failure: Alveolar bone loss 66-100% (ABL, OR 6.48, 95% CI [2.86; 14.89], p<0.001); Periapical index (PAI) score≥4 (OR 4.59, [2.44; 8.79], p<0.001); ABL 33-66% (OR 2.59 [1.49; 4.49], p<0.001); PAI=3 (OR 2.45, [1.43; 4.34], p<0.01); Treatment type "retreatment" (OR 1.77, [1.01; 2.86], p<0.01). On patient level only smoking (OR 2.05, [1.18; 3.53], p<0.05) was significantly associated with risk of failure. For predictive modelling, the predictive power of all models was limited (ROCAUC: logR 0.63, [0.53, 0.73]; GBM 0.59, [0.50, 0.68]; RF 0.59, [0.50, 0.68]; XGB 0.60, [0.50, 0.70]). CONCLUSIONS Failure of RT was associated mainly with tooth-level covariates. Predicting failure was only limitedly possible, also with more complex ML. CLINICAL SIGNIFICANCE Identifying specific risk factors for failure of RT and predicting the outcome of RT is relevant for treatment planning and informed shared decision-making. The present study found tooth-level factors to be associated with failure. Notably, predicting failure was only limitedly possible.
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Sherwood AA, Sherwood AI, Setzer FC, K SD, Shamili JV, John C, Schwendicke F. A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography. J Endod 2021; 47:1907-1916. [PMID: 34563507 DOI: 10.1016/j.joen.2021.09.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 01/11/2023]
Abstract
INTRODUCTION The identification of C-shaped root canal anatomy on radiographic images affects clinical decision making and treatment. The aims of this study were to develop a deep learning (DL) model to classify C-shaped canal anatomy in mandibular second molars from cone-beam computed tomographic (CBCT) volumes and to compare the performance of 3 different architectures. METHODS U-Net, residual U-Net, and Xception U-Net architectures were used for image segmentation and classification of C-shaped anatomies. Model training and validation were performed on 100 of a total of 135 available limited field of view CBCT images containing mandibular molars with C-shaped anatomy. Thirty-five CBCT images were used for testing. Voxel-matching accuracy of the automated labeling of the C-shaped anatomy was assessed with the Dice index. The mean sensitivity of predicting the correct C-shape subcategory was calculated based on detection accuracy. One-way analysis of variance and post hoc Tukey honestly significant difference tests were used for statistical evaluation. RESULTS The mean Dice coefficients were 0.768 ± 0.0349 for Xception U-Net, 0.736 ± 0.0297 for residual U-Net, and 0.660 ± 0.0354 for U-Net on the test data set. The performance of the 3 models was significantly different overall (analysis of variance, P = .000779). Both Xception U-Net (Q = 7.23, P = .00070) and residual U-Net (Q = 5.09, P = .00951) performed significantly better than U-Net (post hoc Tukey honestly significant difference test). The mean sensitivity values were 0.786 ± 0.0378 for Xception U-Net, 0.746 ± 0.0391 for residual U-Net, and 0.720 ± 0.0495 for U-Net. The mean positive predictive values were 77.6% ± 0.1998% for U-Net, 78.2% ± 0.0.1971% for residual U-Net, and 80.0% ± 0.1098% for Xception U-Net. The addition of contrast-limited adaptive histogram equalization had improved overall architecture efficacy by a mean of 4.6% (P < .0001). CONCLUSIONS DL may aid in the detection and classification of C-shaped canal anatomy.
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Affiliation(s)
- Adithya A Sherwood
- Mahatma Montessori Matriculation Higher Secondary School, Madurai, Tamil Nadu, India
| | - Anand I Sherwood
- Department of Conservative Dentistry and Endodontics, CSI College of Dental Sciences, Madurai, Tamil Nadu, India.
| | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Sheela Devi K
- Mahatma Montessori Matriculation Higher Secondary School, Madurai, Tamil Nadu, India
| | - Jasmin V Shamili
- Department of Conservative Dentistry and Endodontics, CSI College of Dental Sciences, Madurai, Tamil Nadu, India
| | - Caroline John
- Department of Computer Science, Hal Marcus College of Science and Engineering, University of West Florida, Pensacola, Florida
| | - Falk Schwendicke
- Department of Oral Diagnostics, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod 2021; 47:1352-1357. [PMID: 34119562 DOI: 10.1016/j.joen.2021.06.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/03/2021] [Accepted: 06/03/2021] [Indexed: 01/04/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to replicate human intelligence to perform prediction and complex decision making in health care and has significantly increased its presence and relevance in various tasks and applications in dentistry, especially endodontics. The aim of this review was to discuss the current endodontic applications of AI and potential future directions. METHODS Articles that have addressed the applications of AI in endodontics were evaluated for information pertinent to include in this narrative review. RESULTS AI models (eg, convolutional neural networks and/or artificial neural networks) have demonstrated various applications in endodontics such as studying root canal system anatomy, detecting periapical lesions and root fractures, determining working length measurements, predicting the viability of dental pulp stem cells, and predicting the success of retreatment procedures. The future of this technology was discussed in light of helping with scheduling, treating patients, drug-drug interactions, diagnosis with prognostic values, and robotic-assisted endodontic surgery. CONCLUSIONS AI demonstrated accuracy and precision in terms of detection, determination, and disease prediction in endodontics. AI can contribute to the improvement of diagnosis and treatment that can lead to an increase in the success of endodontic treatment outcomes. However, it is still necessary to further verify the reliability, applicability, and cost-effectiveness of AI models before transferring these models into day-to-day clinical practice.
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Affiliation(s)
- Anita Aminoshariae
- Department of Endodontics, Case School of Dental Medicine, Cleveland, Ohio.
| | - Jim Kulild
- Department of Endodontics, University of Missouri-Kansas City School of Dentistry, Kansas City, Missouri
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
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Ehtesham H, Safdari R, Mansourian A, Tahmasebian S, Mohammadzadeh N, Pourshahidi S. Developing a new intelligent system for the diagnosis of oral medicine with case-based reasoning approach. Oral Dis 2019; 25:1555-1563. [PMID: 31002445 DOI: 10.1111/odi.13108] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 03/25/2019] [Accepted: 04/03/2019] [Indexed: 01/06/2023]
Abstract
OBJECTIVE Since the clinical manifestations of many oral diseases can be quite similar despite the wide variety in etiology and pathology, the differential diagnosis of oral diseases is a complex and challenging process. Intelligent system for differential diagnosis of oral medicine using the artificial intelligence (AI) capabilities helps specialists in achieving differential diagnosis in a wide range of oral diseases. MATERIALS AND METHODS First, the essential data elements to design and develop an intelligent system were identified in a cross-sectional descriptive study. The case-based reasoning method was selected to design and implement the system, which consists of three stages: collect the clinical data, construct the cases database, and case-based reasoning cycle. The problem is solved by CBR method in a cycle consisting of four main stages of retrieval, reuse, review, and retention. The evaluation process was conducted in a pilot-based way through the evaluation of the system's performance in the clinical setting and also using the usability assessment questionnaire. RESULTS The output of the present project is a web-based intelligent information system, which is developed using the Visual Studio 2015 software. The database of this system is the Microsoft SQL Server version 2012, which has been programmed based on Net framework (version 4.5 or higher) using Visual Basic language. The results of the system evaluation by specialists in clinical settings showed that the system's diagnosis power in different aspects of the disease is influenced by their prevalence and incidence. CONCLUSIONS System development using the artificial intelligence capabilities and through the clinical data analysis has potential to help specialists to determine the best diagnostic strategy to achieve a differential diagnosis of a wide range of oral diseases. The results of evaluation present the potential of the system to improve the quality and efficiency of patient care.
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Affiliation(s)
- Hamideh Ehtesham
- Department of Health Information Technology, Birjand University of Medical Sciences, Birjand, Iran.,School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Safdari
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Arash Mansourian
- Department of Oral Medicine and Dental Research Center, Faculty of Dentistry, Tehran University of Medical Sciences, Tehran, Iran
| | - Shahram Tahmasebian
- School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Niloofar Mohammadzadeh
- Department of Health Information Management, Tehran University of Medical Sciences, Tehran, Iran
| | - Sara Pourshahidi
- Department of Oral and Maxillofacial Medicine, Tehran University of Medical Sciences, Tehran, Iran
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