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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [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: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.
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Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. JOURNAL OF DENTISTRY (SHIRAZ, IRAN) 2023; 24:356-371. [PMID: 38149231 PMCID: PMC10749440 DOI: 10.30476/dentjods.2023.96835.1969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/04/2023] [Accepted: 03/05/2023] [Indexed: 12/28/2023]
Abstract
Statement of the Problem In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation. Purpose Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages. Materials and Method For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence", "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated. Results In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed. Conclusion As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.
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Affiliation(s)
- Kimia Ghods
- Student of Dentistry, Membership of Dental Material Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Arash Azizi
- Dept. Oral Medicine, Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Aryan Jafari
- Student of Dentistry, Membership of Dental Material Research Center, Tehran
| | - Kian Ghods
- Dept. of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada
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Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel) 2023; 11:2760. [PMID: 37893833 PMCID: PMC10606213 DOI: 10.3390/healthcare11202760] [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: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.
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Affiliation(s)
- Junqi Liu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Chengfei Zhang
- Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
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Lee H, Cho JM, Ryu S, Ryu S, Chang E, Jung YS, Kim JY. Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts. Sci Rep 2023; 13:15506. [PMID: 37726392 PMCID: PMC10509166 DOI: 10.1038/s41598-023-42870-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/15/2023] [Indexed: 09/21/2023] Open
Abstract
This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images. Similarly, the constructed artificial intelligence (AI) algorithm automatically identified the landmarks on the images. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the performance of the model. The performance of the model was comparable with that of the examiners. The MRE of the model was 1.87 ± 1.53 mm, and the SDR was 34.7%, 67.5%, and 91.5% within error ranges of < 1.0, < 2.0, and < 4.0 mm, respectively. The sphenoid points and mastoid processes had the lowest MRE and highest SDR in auto-identification; the condyle points had the highest MRE and lowest SDR. Comparable with human examiners, the fully automatic PA cephalometric landmark identification model showed promising accuracy and reliability and can help clinicians perform cephalometric analysis more efficiently while saving time and effort. Future advancements in AI could further improve the model accuracy and efficiency.
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Affiliation(s)
- Hwangyu Lee
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jung Min Cho
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Susie Ryu
- Research and Development Team, Laon Medi Inc., 404 Park B, 723 Pangyo-ro, Bundang-gu, Seongnam-si, 13511, South Korea
| | - Seungmin Ryu
- Department of Orthodontics, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Euijune Chang
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Young-Soo Jung
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jun-Young Kim
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, 03722, South Korea.
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Machine Learning Predictive Model as Clinical Decision Support System in Orthodontic Treatment Planning. Dent J (Basel) 2022; 11:dj11010001. [PMID: 36661538 PMCID: PMC9858447 DOI: 10.3390/dj11010001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/09/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022] Open
Abstract
Diagnosis and treatment planning forms the crux of orthodontics, which orthodontists gain with years of expertise. Machine Learning (ML), having the ability to learn by pattern recognition, can gain this expertise in a very short duration, ensuring reduced error, inter-intra clinician variability and good accuracy. Thus, the aim of this study was to construct an ML predictive model to predict a broader outline of the orthodontic diagnosis and treatment plan. The sample consisted of 700 case records of orthodontically treated patients in the past ten years. The data were split into a training and a test set. There were 33 input variables and 11 output variables. Four ML predictive model layers with seven algorithms were created. The test set was used to check the efficacy of the ML-predicted treatment plan and compared with that of the decision made by the expert orthodontists. The model showed an overall average accuracy of 84%, with the Decision Tree, Random Forest and XGB classifier algorithms showing the highest accuracy ranging from 87-93%. Yet in their infancy stages, Machine Learning models could become a valuable Clinical Decision Support System in orthodontic diagnosis and treatment planning in the future.
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Development, Application, and Performance of Artificial Intelligence in Cephalometric Landmark Identification and Diagnosis: A Systematic Review. Healthcare (Basel) 2022; 10:healthcare10122454. [PMID: 36553978 PMCID: PMC9778374 DOI: 10.3390/healthcare10122454] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/26/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
This study aimed to analyze the existing literature on how artificial intelligence is being used to support the identification of cephalometric landmarks. The systematic analysis of literature was carried out by performing an extensive search in PubMed/MEDLINE, Google Scholar, Cochrane, Scopus, and Science Direct databases. Articles published in the last ten years were selected after applying the inclusion and exclusion criteria. A total of 17 full-text articles were systematically appraised. The Cochrane Handbook for Systematic Reviews of Interventions (CHSRI) and Newcastle-Ottawa quality assessment scale (NOS) were adopted for quality analysis of the included studies. The artificial intelligence systems were mainly based on deep learning-based convolutional neural networks (CNNs) in the included studies. The majority of the studies proposed that AI-based automatic cephalometric analyses provide clinically acceptable diagnostic performance. They have worked remarkably well, with accuracy and precision similar to the trained orthodontist. Moreover, they can simplify cephalometric analysis and provide a quick outcome in practice. Therefore, they are of great benefit to orthodontists, as with these systems they can perform tasks more efficiently.
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Ahn J, Nguyen TP, Kim YJ, Kim T, Yoon J. Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107123. [PMID: 36156440 DOI: 10.1016/j.cmpb.2022.107123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 08/24/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models. MATERIALS AND METHODS Using multiple views extracted from the cone beam computed tomography data of 170 cases as a dataset, our proposed method automatically calculated 13 dental parameters by partitioning, detecting regions of interest, and extracting the facial profile. Subsequently, Mask-RCNN, a trained decentralized convolutional neural network was applied to detect 23 landmarks. All the techniques were integrated into a software application with a graphical user interface designed for user convenience. To demonstrate the system's ability to replace human experts, 30 CBCT data were selected for validation. Two orthodontists and one advanced general dentist located required landmarks by using a commercial dental program. The differences between manual and developed methods were calculated and reported as the errors. RESULTS The intraclass correlation coefficients (ICCs) and 95% confidence interval (95% CI) for intra-observer reliability were 0.98 (0.97-0.99) for observer 1; 0.95 (0.93-0.97) for observer 2; 0.98 (0.97-0.99) for observer 3 after measuring 13 parameters two times at two weeks interval. The combined ICC for intra-observer reliability was 0.97. The ICCs and 95% CI for inter-observer reliability were 0.94 (0.91-0.97). The mean absolute value of deviation was around 1 mm for the length parameters, and smaller than 2° for angle parameters. Furthermore, ANOVA test demonstrated the consistency between the measurements of the proposed method and those of human experts statistically (Fdis=2.68, ɑ=0.05). CONCLUSIONS The proposed system demonstrated the high consistency with the manual measurements of human experts and its applicability. This method aimed to help human experts save time and efforts for analyzing three-dimensional CBCT images of orthodontic patients.
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Affiliation(s)
- Janghoon Ahn
- Department of Orthodontics, Kangnam Sacred Heart Hospital, Hallym University, Singil-ro 1 gil, Yeongdeungpo-gu, Seoul 07441, Republic of Korea
| | - Thong Phi Nguyen
- Department of Mechanical Design Engineering/ Major in Materials, Devices, and Equipment, Hanyang University, 222, Wangsimni-ro, Seongdongsu, Seoul 04763, Republic of Korea; BK21 FOUR ERICA-ACE Centre, Hanyang University, Ansan-si, Gyeonggi-do 15588, Republic of Korea
| | - Yoon-Ji Kim
- Department of Orthodontics, Asan Medical Centre, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505 Republic of Korea
| | - Taeyong Kim
- Department of Advanced General Dentistry, Kangnam Sacred Heart Hospital, Hallym University, Singil-ro 1-gil, Yeongdeungpo-gu, Seoul 07441, Republic of Korea
| | - Jonghun Yoon
- Department of Mechanical Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do 15588, Republic of Korea; BK21 FOUR ERICA-ACE Centre, Hanyang University, Ansan-si, Gyeonggi-do 15588, Republic of Korea.
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Gil SM, Kim I, Cho JH, Hong M, Kim M, Kim SJ, Kim YJ, Kim YH, Lim SH, Sung SJ, Baek SH, Kim N, Kang KH. Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers. Am J Orthod Dentofacial Orthop 2022; 161:e361-e371. [DOI: 10.1016/j.ajodo.2021.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 12/01/2022]
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Monill-González A, Rovira-Calatayud L, d'Oliveira NG, Ustrell-Torrent JM. Artificial intelligence in orthodontics: Where are we now? A scoping review. Orthod Craniofac Res 2021; 24 Suppl 2:6-15. [PMID: 34270881 DOI: 10.1111/ocr.12517] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 06/29/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. MATERIALS AND METHODS This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11-year time restriction: January 2010 till March 2021. No additional manual searches were performed. RESULTS The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. CONCLUSION The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.
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Affiliation(s)
- Anna Monill-González
- Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | | | - Nuno Gustavo d'Oliveira
- Department of Odontostomatology - Orthodontics. Coordinator of the Master of Orthodontics, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Josep M Ustrell-Torrent
- Department of Odontostomatology - Orthodontics, Oral Health and Masticatory System Group (IDIBELL), University of Barcelona, Barcelona, Spain
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