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de Oliveira PHJ, Li T, Li H, Gonçalves JR, Santos-Pinto A, Gandini Junior LG, Cevidanes LS, Toyama C, Feltrin GP, Campanha AA, de Oliveira Junior MA, Bianchi J. Artificial intelligence as a prediction tool for orthognathic surgery assessment. Orthod Craniofac Res 2024; 27:785-794. [PMID: 38715428 DOI: 10.1111/ocr.12805] [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] [Accepted: 04/21/2024] [Indexed: 10/11/2024]
Abstract
INTRODUCTION An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodontics and orthognathic surgery remains challenging, especially in borderline patients. Advances in technology are aiding clinical decisions in orthodontics. The increasing availability of data and the era of big data enable the use of artificial intelligence to guide clinicians' diagnoses. This study aims to test the capacity of different machine learning (ML) models to predict whether orthognathic surgery or orthodontics treatment is required, using soft and hard tissue cephalometric values. METHODS A total of 920 lateral radiographs from patients previously treated with either conventional orthodontics or in combination with orthognathic surgery were used, comprising n = 558 Class II and n = 362 Class III patients, respectively. Thirty-two measures were obtained from each cephalogram at the initial appointment. The subjects were randomly divided into training (n = 552), validation (n = 183), and test (n = 185) datasets, both as an entire sample and divided into Class II and Class III sub-groups. The extracted data were evaluated using 10 machine learning models and by a four-expert panel consisting of orthodontists (n = 2) and surgeons (n = 2). RESULTS The combined prediction of 10 models showed top-ranked performance in the testing dataset for accuracy, F1-score, and AUC (entire sample: 0.707, 0.706, 0.791; Class II: 0.759, 0.758, 0.824; Class III: 0.822, 0.807, 0.89). CONCLUSIONS The proposed combined 10 ML approach model accurately predicted the need for orthognathic surgery, showing better performance in Class III patients.
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Affiliation(s)
- Pedro Henrique José de Oliveira
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry, São Paulo State University (Unesp), Araraquara, São Paulo, Brazil
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Haoyue Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - João Roberto Gonçalves
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry, São Paulo State University (Unesp), Araraquara, São Paulo, Brazil
| | - Ary Santos-Pinto
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry, São Paulo State University (Unesp), Araraquara, São Paulo, Brazil
| | - Luiz Gonzaga Gandini Junior
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry, São Paulo State University (Unesp), Araraquara, São Paulo, Brazil
| | - Lucia Soares Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | | | | | | | | | - Jonas Bianchi
- Department of Morphology, Genetics, Orthodontics and Pediatric Dentistry, School of Dentistry, São Paulo State University (Unesp), Araraquara, São Paulo, Brazil
- Department of Orthodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, San Francisco, California, USA
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Hori M, Jincho M, Hori T, Sekine H, Kato A, Miyazawa K, Kawai T. Automatic point detection on cephalograms using convolutional neural networks: A two-step method. Dent Mater J 2024; 43:701-710. [PMID: 39231691 DOI: 10.4012/dmj.2024-052] [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: 09/06/2024]
Abstract
This project aimed to develop an artificial intelligence program tailored for cephalometric images. The program employs a convolutional neural network with 6 convolutional layers and 2 affine layers. It identifies 18 key points on the skull to compute various angles essential for diagnosis. Utilizing a custom-built desktop computer with a moderately priced graphics processing unit, cephalogram images were resized to 800×800 pixels. Training data comprised 833 images, augmented 100 times; an additional 179 images were used for testing. Due to the complexity of training with full-size images, training was divided into two steps. The first step reduced images to 128×128 pixels, recognizing all 18 points. In the second step, 100×100 pixels blocks were extracted from original images for individual point training. The program then measured six angles, achieving an average error of 3.1 pixels for the 18 points, with SNA and SNB angles showing an average difference of less than 1°.
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Affiliation(s)
- Miki Hori
- Department of Dental Materials Science, School of Dentistry, Aichi Gakuin University
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Makoto Jincho
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Tadasuke Hori
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Hironao Sekine
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Akiko Kato
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
- Department of Oral Anatomy, School of Dentistry, Aichi Gakuin University
| | - Ken Miyazawa
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
- Department of Orthodontics, School of Dentistry, Aichi Gakuin University
| | - Tatsushi Kawai
- Department of Dental Materials Science, School of Dentistry, Aichi Gakuin University
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
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Park JA, Moon JH, Lee JM, Cho SJ, Seo BM, Donatelli RE, Lee SJ. Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods? Angle Orthod 2024; 94:549-556. [PMID: 39230019 PMCID: PMC11363980 DOI: 10.2319/111423-756.1] [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: 11/01/2023] [Accepted: 03/01/2024] [Indexed: 09/05/2024] Open
Abstract
OBJECTIVES To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. MATERIALS AND METHODS Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. RESULTS In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. CONCLUSIONS AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.
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Affiliation(s)
| | | | | | | | | | | | - Shin-Jae Lee
- Corresponding author: Dr Shin-Jae Lee, Professor, Dental Research Institute, Seoul National University School of Dentistry, Jongro-Gu, Seoul 03080, Korea (e-mail: )
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Cho SJ, Moon JH, Ko DY, Lee JM, Park JA, Donatelli RE, Lee SJ. Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods. Angle Orthod 2024; 94:557-565. [PMID: 39230022 PMCID: PMC11363978 DOI: 10.2319/111823-767.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/01/2024] [Indexed: 09/05/2024] Open
Abstract
OBJECTIVES To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. MATERIALS AND METHODS A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. RESULTS Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. CONCLUSIONS When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods.
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Affiliation(s)
| | | | | | | | | | | | - Shin-Jae Lee
- Corresponding author: Dr Shin-Jae Lee, Professor, Department of Orthodontics and Dental Research Institute, Seoul National University School of Dentistry, Jongro-Gu, Seoul 03080, Korea (e-mail: )
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Hendrickx J, Gracea RS, Vanheers M, Winderickx N, Preda F, Shujaat S, Jacobs R. Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis. Eur J Orthod 2024; 46:cjae029. [PMID: 38895901 PMCID: PMC11185929 DOI: 10.1093/ejo/cjae029] [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: 06/21/2024]
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. SEARCH METHODS An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. SELECTION CRITERIA Studies that employed AI for 2D or 3D cephalometric landmark detection were included. DATA COLLECTION AND ANALYSIS The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. RESULTS Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). CONCLUSION The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. REGISTRATION PROSPERO: CRD42022328800.
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Affiliation(s)
- Julie Hendrickx
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Rellyca Sola Gracea
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Michiel Vanheers
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Nicolas Winderickx
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Flavia Preda
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 14611, Kingdom of Saudi Arabia
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- Department of Dental Medicine, Karolinska Institutet, 141 04 Stockholm, Sweden
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Chang Q, Bai Y, Wang S, Wang F, Wang Y, Zuo F, Xie X. Automatic soft-tissue analysis on orthodontic frontal and lateral facial photographs based on deep learning. Orthod Craniofac Res 2024. [PMID: 38967085 DOI: 10.1111/ocr.12830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND To establish the automatic soft-tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft tissues. METHODS A total of 578 frontal photographs and 450 lateral photographs of orthodontic patients were collected to construct datasets. All images were manually annotated by two orthodontists with 43 frontal-image landmarks and 17 lateral-image landmarks. Automatic landmark detection models were established, which consisted of a high-resolution network, a feature fusion module based on depthwise separable convolution, and a prediction model based on pixel shuffle. Ten measurements for frontal images and eight measurements for lateral images were defined. Test sets were used to evaluate the model performance, respectively. The mean radial error of landmarks and measurement error were calculated and statistically analysed to evaluate their reliability. RESULTS The mean radial error was 14.44 ± 17.20 pixels for the landmarks in the frontal images and 13.48 ± 17.12 pixels for the landmarks in the lateral images. There was no statistically significant difference between the model prediction and manual annotation measurements except for the mid facial-lower facial height index. A total of 14 measurements had a high consistency. CONCLUSION Based on deep learning, we established automatic soft-tissue analysis models for orthodontic facial photographs that can automatically detect 43 frontal-image landmarks and 17 lateral-image landmarks while performing comprehensive soft-tissue measurements. The models can assist orthodontists in efficient and accurate quantitative soft-tissue evaluation for clinical application.
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Affiliation(s)
- Qiao Chang
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing, China
| | - Yuxing Bai
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing, China
| | - Shaofeng Wang
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing, China
| | - Fan Wang
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing, China
| | - Yajie Wang
- Department of Engineering Physics, Tsinghua University, Beijing, China
- LargeV Instrument Corporation Limited, Beijing, China
| | - Feifei Zuo
- LargeV Instrument Corporation Limited, Beijing, China
| | - Xianju Xie
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing, China
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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [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: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Kazimierczak W, Gawin G, Janiszewska-Olszowska J, Dyszkiewicz-Konwińska M, Nowicki P, Kazimierczak N, Serafin Z, Orhan K. Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics. J Clin Med 2024; 13:3733. [PMID: 38999299 PMCID: PMC11242750 DOI: 10.3390/jcm13133733] [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: 06/11/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph. This study involved a retrospective analysis of lateral cephalograms from a single orthodontic center. Automated CA was performed using the AI programs, focusing on common parameters defined by Downs, Ricketts, and Steiner. Repeatability was tested through 50 randomly reanalyzed cases by each software. Statistical analyses included intraclass correlation coefficients (ICC3) for agreement and the Friedman test for concordance. Results: One hundred twenty-four cephalograms were analyzed. High agreement between the AI systems was noted for most parameters (ICC3 > 0.9). Notable differences were found in the measurements of angle convexity and the occlusal plane, where discrepancies suggested different methodologies among the programs. Some analyses presented high variability in the results, indicating errors. Repeatability analysis revealed perfect agreement within each program. Conclusions: AI-driven cephalometric analysis tools demonstrate a high potential for reliable and efficient orthodontic assessments, with substantial agreement in repeated analyses. Despite this, the observed discrepancies and high variability in part of analyses underscore the need for standardization across AI platforms and the critical evaluation of automated results by clinicians, particularly in parameters with significant treatment implications.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Grzegorz Gawin
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | | | | | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Kaan Orhan
- Department of DentoMaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06500, Turkey
- Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara 06500, Turkey
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, 1085 Budapest, Hungary
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Sahlsten J, Järnstedt J, Jaskari J, Naukkarinen H, Mahasantipiya P, Charuakkra A, Vasankari K, Hietanen A, Sundqvist O, Lehtinen A, Kaski K. Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset. PLoS One 2024; 19:e0305947. [PMID: 38917161 PMCID: PMC11198780 DOI: 10.1371/journal.pone.0305947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/07/2024] [Indexed: 06/27/2024] Open
Abstract
Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT scans. However, these approaches have relied on uniform datasets from a single center or imaging device but without considering patient ethnicity. In addition, previous works have considered a limited number of clinically relevant cephalometric landmarks and the approaches were computationally infeasible, both impairing integration into clinical workflow. Here our aim is to analyze the clinical applicability of a light-weight deep learning neural network for fast localization of 46 clinically significant cephalometric landmarks with multi-center, multi-ethnic, and multi-device data consisting of 309 CBCT scans from Finnish and Thai patients. The localization performance of our approach resulted in the mean distance of 1.99 ± 1.55 mm for the Finnish cohort and 1.96 ± 1.25 mm for the Thai cohort. This performance turned out to be clinically significant i.e., ≤ 2 mm with 61.7% and 64.3% of the landmarks with Finnish and Thai cohorts, respectively. Furthermore, the estimated landmarks were used to measure cephalometric characteristics successfully i.e., with ≤ 2 mm or ≤ 2° error, on 85.9% of the Finnish and 74.4% of the Thai cases. Between the two patient cohorts, 33 of the landmarks and all cephalometric characteristics had no statistically significant difference (p < 0.05) measured by the Mann-Whitney U test with Benjamini-Hochberg correction. Moreover, our method is found to be computationally light, i.e., providing the predictions with the mean duration of 0.77 s and 2.27 s with single machine GPU and CPU computing, respectively. Our findings advocate for the inclusion of this method into clinical settings based on its technical feasibility and robustness across varied clinical datasets.
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Affiliation(s)
- Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Jorma Järnstedt
- Department of Radiology, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | | | - Phattaranant Mahasantipiya
- Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand
- Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand
| | - Arnon Charuakkra
- Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand
- Division of Oral and Maxillofacial Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand
| | - Krista Vasankari
- Department of Oral Diseases, Tampere University Hospital, Tampere, Finland
| | | | | | - Antti Lehtinen
- Department of Radiology, Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
- The Alan Turing Institute, British Library, London, United Kingdom
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Zhao L, Chen X, Huang J, Mo S, Gu M, Kang N, Song S, Zhang X, Liang B, Tang M. Machine Learning Algorithms for the Diagnosis of Class III Malocclusions in Children. CHILDREN (BASEL, SWITZERLAND) 2024; 11:762. [PMID: 39062212 PMCID: PMC11274672 DOI: 10.3390/children11070762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/13/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024]
Abstract
Artificial intelligence has been applied to medical diagnosis and decision-making but it has not been used for classification of Class III malocclusions in children. OBJECTIVE This study aims to propose an innovative machine learning (ML)-based diagnostic model for automatically classifies dental, skeletal and functional Class III malocclusions. METHODS The collected data related to 46 cephalometric feature measurements from 4-14-year-old children (n = 666). The data set was divided into a training set and a test set in a 7:3 ratio. Initially, we employed the Recursive Feature Elimination (RFE) algorithm to filter the 46 input parameters, selecting 14 significant features. Subsequently, we constructed 10 ML models and trained these models using the 14 significant features from the training set through ten-fold cross-validation, and evaluated the models' average accuracy in test set. Finally, we conducted an interpretability analysis of the optimal model using the ML model interpretability tool SHapley Additive exPlanations (SHAP). RESULTS The top five models ranked by their area under the curve (AUC) values were: GPR (0.879), RBF SVM (0.876), QDA (0.876), Linear SVM (0.875) and L2 logistic (0.869). The DeLong test showed no statistical difference between GPR and the other models (p > 0.05). Therefore GPR was selected as the optimal model. The SHAP feature importance plot revealed that he top five features were SN-GoMe (the ratio of the length of the anterior skull base SN to that of the mandibular base GoMe), U1-NA (maxillary incisor angulation to NA plane), Overjet (the distance between two lines perpendicular to the functional occlusal plane from U1 and L), ANB (the difference between angles SNA and SNB), and AB-NPo (the angle between the AB and N-Pog line). CONCLUSIONS Our findings suggest that ML models based on cephalometric data could effectively assist dentists to classify dental, functional and skeletal Class III malocclusions in children. In addition, features such as SN_GoMe, U1_NA and Overjet can as important indicators for predicting the severity of Class III malocclusions.
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Affiliation(s)
- Ling Zhao
- Department of Orthodontics, Guangxi Medical University College of Stomatology, Nanning 530021, China; (L.Z.); (S.M.); (N.K.); (S.S.)
| | - Xiaozhi Chen
- Department of Stomatology, Guangxi Chinese-Traditional Medical University, Nanning 530021, China;
| | - Juneng Huang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (J.H.); (X.Z.); (B.L.)
| | - Shuixue Mo
- Department of Orthodontics, Guangxi Medical University College of Stomatology, Nanning 530021, China; (L.Z.); (S.M.); (N.K.); (S.S.)
| | - Min Gu
- Department of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China;
| | - Na Kang
- Department of Orthodontics, Guangxi Medical University College of Stomatology, Nanning 530021, China; (L.Z.); (S.M.); (N.K.); (S.S.)
| | - Shaohua Song
- Department of Orthodontics, Guangxi Medical University College of Stomatology, Nanning 530021, China; (L.Z.); (S.M.); (N.K.); (S.S.)
| | - Xuejun Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (J.H.); (X.Z.); (B.L.)
| | - Bohui Liang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China; (J.H.); (X.Z.); (B.L.)
| | - Min Tang
- Department of Orthodontics, Guangxi Medical University College of Stomatology, Nanning 530021, China; (L.Z.); (S.M.); (N.K.); (S.S.)
- Guangxi Clinical Research Center for Craniofacial Deformity, Nanning 530021, China
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11
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Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus 2024; 16:e62045. [PMID: 38989357 PMCID: PMC11234326 DOI: 10.7759/cureus.62045] [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: 03/03/2024] [Accepted: 06/09/2024] [Indexed: 07/12/2024] Open
Abstract
Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.
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Affiliation(s)
- Aathira Surendran
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Pallavi Daigavane
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Sunita Shrivastav
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Ranjit Kamble
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Abhishek D Sanchla
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Lovely Bharti
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Mrudula Shinde
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
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12
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Butul B, Sharab L. Obstacles behind the innovation- a peek into Artificial intelligence in the field of orthodontics - A Literature review. Saudi Dent J 2024; 36:830-834. [PMID: 38883898 PMCID: PMC11178964 DOI: 10.1016/j.sdentj.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 02/14/2024] [Accepted: 03/11/2024] [Indexed: 06/18/2024] Open
Abstract
This article explores the potential benefits of Artificial Intelligence (AI) and Machine Learning (ML) in Orthodontics, highlighting their efficiency and accuracy. While AI has influenced various fields, its application in orthodontics is just being explored. With the innovation comes challenges that are associated with AI. This article emphasizes the documented role of AI and its associated barriers in Orthodontics. Methods Literature research is performed in data sources like online library journals PubMed and MEDLINE, NIH (National Institute of Health), Science Direct, WILEY online library, and ORAL HEALTH GROUP, among others. Our review was carried out on articles published to date. Conclusion The findings in this review highlight the considerable promise of employing AI within orthodontics. However, the emergence of AI also brings forth fresh challenges that must be considered. Striking a balance between innovation and addressing these challenges is crucial for advancing orthodontics.
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Affiliation(s)
- Bushra Butul
- Department of Oral Health Science, Division of Orthodontics, University of Kentucky College of Dentistry, 800 Rose Street, Lexington, KY, USA
| | - Lina Sharab
- Department of Oral Health Science, Division of Orthodontics, University of Kentucky College of Dentistry, 800 Rose Street, Lexington, KY, USA
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13
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Lee JM, Moon JH, Park JA, Kim JH, Lee SJ. Factors influencing the development of artificial intelligence in orthodontics. Orthod Craniofac Res 2024. [PMID: 38712670 DOI: 10.1111/ocr.12806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
OBJECTIVES Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. MATERIALS AND METHODS A total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors. RESULTS Increasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99. CONCLUSION The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.
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Affiliation(s)
| | | | - Ji-Ae Park
- Department of Orthodontics, Seoul National University Dental Hospital, Seoul, Korea
| | | | - Shin-Jae Lee
- Department of Orthodontics and Dental Research Institute, Seoul National University School of Dentistry, Seoul, Korea
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14
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Guinot-Barona C, Alonso Pérez-Barquero J, Galán López L, Barmak AB, Att W, Kois JC, Revilla-León M. Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs. J ESTHET RESTOR DENT 2024; 36:555-565. [PMID: 37882509 DOI: 10.1111/jerd.13156] [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: 06/16/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability. MATERIALS AND METHODS A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05). RESULTS The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. CONCLUSIONS Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. CLINICAL SIGNIFICANCE The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.
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Affiliation(s)
- Clara Guinot-Barona
- Department of Dental Orthodontics, Faculty of Medicine and Health Sciences, Catholic University of Valencia, Valencia, Spain
| | | | - Lidia Galán López
- Department of Dental Orthodontics, Faculty of Medicine and Health Sciences, Catholic University of Valencia, Valencia, Spain
| | - Abdul B Barmak
- Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, New York, USA
| | - Wael Att
- Department of Prosthodontics, University Hospital of Freiburg, Freiburg, Germany, USA
| | - John C Kois
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Private Practice, Seattle, Washington, USA
| | - Marta Revilla-León
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA
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15
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Silva TP, Pinheiro MCR, Freitas DQ, Gaêta-Araujo H, Oliveira-Santos C. Assessment of accuracy and reproducibility of cephalometric identification performed by 2 artificial intelligence-driven tracing applications and human examiners. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137:431-440. [PMID: 38365543 DOI: 10.1016/j.oooo.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 01/04/2024] [Accepted: 01/13/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE To assess the accuracy and reproducibility of cephalometric landmark identification performed by 2 artificial intelligence (AI)-driven applications (CefBot and WebCeph) and human examiners. STUDY DESIGN Lateral cephalometric radiographs of 10 skulls containing 0.5 mm lead spheres directly placed at 10 cephalometric landmarks were obtained as the reference standard. Ten radiographs without spheres were obtained from the same skulls for identification of cephalometric points performed by the AI applications and 10 examiners. The x- and y-coordinate values of the cephalometric points identified by the AI applications and examiners were compared with those from the reference standard images using one-way analysis of variance and the Dunnet post-hoc test. The intraclass correlation coefficient (ICC) was used to evaluate reproducibility. Mean radial error (MRE) in identification was calculated with respect to the reference standard. Statistical significance was established at P < .05. RESULTS Landmark identification by CefBot and the examiners did not exhibit significant differences from the reference standard on either axis (P > .05). WebCeph produced a significant difference (P < .05) in 4 and 6 points on the x- and y-axes, respectively. Reproducibility was excellent for CefBot and the examiners (ICC ≥ 0.9943) and good for WebCeph (ICC ≥ 0.7868). MREs of CefBot and the examiners were similar. CONCLUSION With results similar to those of human examiners, CefBot demonstrated excellent reliability and can aid in cephalometric applications. WebCeph produced significant errors.
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Affiliation(s)
- Thaísa Pinheiro Silva
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, Brazil.
| | - Maria Clara Rodrigues Pinheiro
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, Brazil
| | - Deborah Queiroz Freitas
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, Brazil
| | - Hugo Gaêta-Araujo
- Department of Stomatology, Public Oral Health, Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirao Preto, University of Sao Paulo (USP), Ribeirao Preto, Sao Paulo, Brazil
| | - Christiano Oliveira-Santos
- Department of Diagnosis and Oral Health, University of Louisville School of Dentistry, Louisville, KY, USA
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16
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Ahn HJ, Byun SH, Baek SH, Park SY, Yi SM, Park IY, On SW, Kim JC, Yang BE. A Comparative Analysis of Artificial Intelligence and Manual Methods for Three-Dimensional Anatomical Landmark Identification in Dentofacial Treatment Planning. Bioengineering (Basel) 2024; 11:318. [PMID: 38671740 PMCID: PMC11048285 DOI: 10.3390/bioengineering11040318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
With the growing demand for orthognathic surgery and other facial treatments, the accurate identification of anatomical landmarks has become crucial. Recent advancements have shifted towards using three-dimensional radiologic analysis instead of traditional two-dimensional methods, as it allows for more precise treatment planning, primarily relying on direct identification by clinicians. However, manual tracing can be time-consuming, mainly when dealing with a large number of patients. This study compared the accuracy and reliability of identifying anatomical landmarks using artificial intelligence (AI) and manual identification. Thirty patients over 19 years old who underwent pre-orthodontic and orthognathic surgery treatment and had pre-orthodontic three-dimensional radiologic scans were selected. Thirteen anatomical indicators were identified using both AI and manual methods. The landmarks were identified by AI and four experienced clinicians, and multiple ANOVA was performed to analyze the results. The study results revealed minimal significant differences between AI and manual tracing, with a maximum deviation of less than 2.83 mm. This indicates that utilizing AI to identify anatomical landmarks can be a reliable method in planning orthognathic surgery. Our findings suggest that using AI for anatomical landmark identification can enhance treatment accuracy and reliability, ultimately benefiting clinicians and patients.
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Affiliation(s)
- Hee-Ju Ahn
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (H.-J.A.); (S.-H.B.); (S.-H.B.); (S.-Y.P.); (S.-M.Y.); (J.-C.K.)
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Dental Artificial Intelligence and Robotics R&D Center, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
| | - Soo-Hwan Byun
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (H.-J.A.); (S.-H.B.); (S.-H.B.); (S.-Y.P.); (S.-M.Y.); (J.-C.K.)
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Dental Artificial Intelligence and Robotics R&D Center, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
| | - Sae-Hoon Baek
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (H.-J.A.); (S.-H.B.); (S.-H.B.); (S.-Y.P.); (S.-M.Y.); (J.-C.K.)
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Dental Artificial Intelligence and Robotics R&D Center, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
| | - Sang-Yoon Park
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (H.-J.A.); (S.-H.B.); (S.-H.B.); (S.-Y.P.); (S.-M.Y.); (J.-C.K.)
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Dental Artificial Intelligence and Robotics R&D Center, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
| | - Sang-Min Yi
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (H.-J.A.); (S.-H.B.); (S.-H.B.); (S.-Y.P.); (S.-M.Y.); (J.-C.K.)
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Dental Artificial Intelligence and Robotics R&D Center, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
| | - In-Young Park
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Dental Artificial Intelligence and Robotics R&D Center, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
- Department of Orthodontics, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
| | - Sung-Woon On
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Division of Oral and Maxillofacial Surgery, Department of Dentistry, Hallym University Dongtan Sacred Heart Hospital, Hawseong 18450, Republic of Korea
| | - Jong-Cheol Kim
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (H.-J.A.); (S.-H.B.); (S.-H.B.); (S.-Y.P.); (S.-M.Y.); (J.-C.K.)
- Mir Dental Hospital, Daegu 41940, Republic of Korea
| | - Byoung-Eun Yang
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea; (H.-J.A.); (S.-H.B.); (S.-H.B.); (S.-Y.P.); (S.-M.Y.); (J.-C.K.)
- Department of Artificial Intelligence and Robotics in Dentistry, Graduate School of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea; (I.-Y.P.); (S.-W.O.)
- Institute of Clinical Dentistry, Hallym University, Chuncheon 24252, Republic of Korea
- Dental Artificial Intelligence and Robotics R&D Center, Hallym University Sacred Heart Hospital, Anyang 14068, Republic of Korea
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Berends B, Bielevelt F, Schreurs R, Vinayahalingam S, Maal T, de Jong G. Fully automated landmarking and facial segmentation on 3D photographs. Sci Rep 2024; 14:6463. [PMID: 38499700 PMCID: PMC10948387 DOI: 10.1038/s41598-024-56956-9] [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: 11/17/2023] [Accepted: 03/13/2024] [Indexed: 03/20/2024] Open
Abstract
Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.
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Affiliation(s)
- Bo Berends
- 3D Lab Radboudumc, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
| | - Freek Bielevelt
- 3D Lab Radboudumc, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Ruud Schreurs
- 3D Lab Radboudumc, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Department of Oral and Maxillofacial Surgery, Amsterdam University Medical Center (UMC), AMC, Academic Center for Dentistry Amsterdam (ACTA), Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center Nijmegen, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Thomas Maal
- 3D Lab Radboudumc, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Guido de Jong
- 3D Lab Radboudumc, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
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18
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Moon JH, Shin HK, Lee JM, Cho SJ, Park JA, Donatelli RE, Lee SJ. Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence. Angle Orthod 2024; 94:207-215. [PMID: 37913813 PMCID: PMC10893918 DOI: 10.2319/031723-181.1] [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: 03/01/2023] [Accepted: 09/01/2023] [Indexed: 11/03/2023] Open
Abstract
OBJECTIVES To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI). MATERIALS AND METHODS Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared. RESULTS On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively. CONCLUSIONS PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.
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19
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Zhou G, Zhang Y, Zhao J, Tian L, Jia G, Ma Q. A rapid identification method for soft tissue markers of dentofacial deformities based on heatmap regression. BDJ Open 2024; 10:14. [PMID: 38429260 PMCID: PMC10907697 DOI: 10.1038/s41405-024-00189-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/19/2023] [Accepted: 01/08/2024] [Indexed: 03/03/2024] Open
Abstract
OBJECTIVE The purpose of this study was to construct a facial deformity dataset and a network model based on heatmap regression for the recognition of facial soft tissue landmarks to provide a basis for clinicians to perform cephalometric analysis of soft tissue. MATERIALS AND METHODS A 34-point face marker detection model, the Back High-Resolution Network (BHR-Net), was constructed based on the heatmap regression algorithm, and a custom dataset of 1780 facial detection images for orthognathic surgery was collected. The mean normalized error (MNE) and 10% failure rate (FR10%) were used to evaluate the performance of BHR-Net, and a test set of 50 patients was used to verify the accuracy of the landmarks and their measurement indicators. The test results were subsequently validated in 30 patients. RESULTS Both the MNE and FR10% of BHR-Net were optimal compared with other models. In the test set (50 patients), the accuracy of the markers excluding the nose root was 86%, and the accuracy of the remaining markers reached 94%. In the model validation (30 patients), using the markers detected by BHR-Net, the diagnostic accuracy of doctors was 100% for Class II and III deformities, 100% for the oral angle plane, and 70% for maxillofacial asymmetric deformities. CONCLUSIONS BHR-Net, a network model based on heatmap regression, can be used to effectively identify landmarks in maxillofacial multipose images, providing a reliable way for clinicians to perform cephalometric measurements of soft tissue objectively and quickly.
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Affiliation(s)
- Guilong Zhou
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China
- Hospital 987, Joint Logistics Support Force, 721000, Baoji, China
| | - Yu Zhang
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, China
| | - Jinlong Zhao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China
| | - Lei Tian
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China.
- Oral Biomechanics Basic and Clinical Research Innovation Team, 710032, Xi'an, China.
| | - Guang Jia
- School of Computer Science and Technology, Xidian University, 710071, Xi'an, China.
| | - Qin Ma
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Centre for Oral Diseases, Shaanxi Clinical Research Centre for Oral Diseases, Department of Orthognathic Trauma Surgery, The Third Affiliated Hospital of Air Force Medical University, 710032, Xi'an, China.
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20
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Ayhan B, Ayan E, Bayraktar Y. A novel deep learning-based perspective for tooth numbering and caries detection. Clin Oral Investig 2024; 28:178. [PMID: 38411726 PMCID: PMC10899376 DOI: 10.1007/s00784-024-05566-w] [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: 11/15/2023] [Accepted: 02/17/2024] [Indexed: 02/28/2024]
Abstract
OBJECTIVES The aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms. METHODS The dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process. RESULTS According to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1-Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively. CONCLUSIONS The proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently. CLINICAL SIGNIFICANCE CNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.
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Affiliation(s)
- Baturalp Ayhan
- Department of Restorative Dentistry, Faculty of Dentistry, Kırıkkale University, Kırıkkale, Turkey.
| | - Enes Ayan
- Department of Computer Engineering, Faculty of Engineering and Architecture, Kırıkkale University, Kırıkkale, Turkey
| | - Yusuf Bayraktar
- Department of Restorative Dentistry, Faculty of Dentistry, Kırıkkale University, Kırıkkale, Turkey
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21
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Han SH, Lim J, Kim JS, Cho JH, Hong M, Kim M, Kim SJ, Kim YJ, Kim YH, Lim SH, Sung SJ, Kang KH, Baek SH, Choi SK, Kim N. Accuracy of posteroanterior cephalogram landmarks and measurements identification using a cascaded convolutional neural network algorithm: A multicenter study. Korean J Orthod 2024; 54:48-58. [PMID: 38072448 PMCID: PMC10811357 DOI: 10.4041/kjod23.075] [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: 03/31/2023] [Revised: 09/07/2023] [Accepted: 10/10/2023] [Indexed: 01/26/2024] Open
Abstract
Objective : To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods : A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results : The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions : The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.
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Affiliation(s)
- Sung-Hoon Han
- Department of Orthodontics, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jisup Lim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jun-Sik Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jin-Hyoung Cho
- Department of Orthodontics, School of Dentistry, Chonnam National University, Gwangju, Korea
| | - Mihee Hong
- Department of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, Korea
| | - Minji Kim
- Department of Orthodontics, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Su-Jung Kim
- Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Korea
| | - Yoon-Ji Kim
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Ho Kim
- Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon, Korea
| | - Sung-Hoon Lim
- Department of Orthodontics, College of Dentistry, Chosun University, Gwangju, Korea
| | - Sang Jin Sung
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyung-Hwa Kang
- Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan, Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Korea
| | - Sung-Kwon Choi
- Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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22
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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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23
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Gupta S, Shetty S, Natarajan S, Nambiar S, Mv A, Agarwal S. A comparative evaluation of concordance and speed between smartphone app-based and artificial intelligence web-based cephalometric tracing software with the manual tracing method: A cross-sectional study. J Clin Exp Dent 2024; 16:e11-e17. [PMID: 38314342 PMCID: PMC10837802 DOI: 10.4317/jced.60899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/18/2023] [Indexed: 02/06/2024] Open
Abstract
Background This study compared the accuracy and speed of cephalometric analysis using an artificial intelligence web-based method and a smartphone app-based system with manual cephalometric analysis as the reference standard. Material and Methods In this cross-sectional study, the lateral cephalograms were analysed using four methods: manual tracing, smartphone app tracing, artificial intelligence web-based automated tracing without manual landmark identification correction and artificial intelligence web-based automated tracing with manual landmark identification correction. The principal investigator obtained linear and angular cephalometric measurements to compare the accuracies of the four methods being assessed. Additionally, the duration required for landmark identification and subsequent analysis was recorded. Results The analyses included 40 lateral cephalograms that were selected based on the inclusion and exclusion criteria. Very good to excellent agreement was observed in the accuracies of the artificial intelligence web-based and smartphone app-based systems compared with manual tracing (interclass correlation coefficient values ranging from 0.707 to 0.9, p< 0.001). Of the artificial intelligence web-based systems, the method without correction of automated landmark detection showed less reliable measurements than the other methods. Cephalometric analysis using artificial intelligence web-based and smartphone app-based systems consumed less time than manual tracing (p< 0.001). Conclusions Artificial intelligence web-based automated tracing with manual landmark identification correction and smartphone-based app provide results that are comparable to those from the manual tracing method. However, artificial intelligence web-based systems require improvements in terms of automated landmark identification to obtain results that are similar to those from the other methods being assessed. Key words:Artificial Intelligence, Cephalometry, Computer software, Mobile application.
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Affiliation(s)
- Shantam Gupta
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Shravan Shetty
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Srikant Natarajan
- Department of Oral Pathology and Microbiology, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Supriya Nambiar
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Ashith Mv
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Saloni Agarwal
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India
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Jeon SM, Kim S, Lee KC. Deep Learning-based Assessment of Facial Asymmetry Using U-Net Deep Convolutional Neural Network Algorithm. J Craniofac Surg 2024; 35:133-136. [PMID: 37973054 DOI: 10.1097/scs.0000000000009862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/09/2023] [Indexed: 11/19/2023] Open
Abstract
OBJECTIVES This study aimed to evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system to detect facial asymmetry on posteroanterior (PA) cephalograms and compare the results of the DCNN with those made by the orthodontist. MATERIALS AND METHODS PA cephalograms of 1020 patients with orthodontics were used to train the DCNN-based CAD systems for autoassessment of facial asymmetry, the degree of menton deviation, and the coordinates of its regarding landmarks. Twenty-five PA cephalograms were used to test the performance of the DCNN in analyzing facial asymmetry. The diagnostic performance of the DCNN-based CAD system was assessed using independent t -tests and Bland-Altman plots. RESULTS Comparison between the DCNN-based CAD system and conventional analysis confirmed no significant differences. Bland-Altman plots showed good agreement for all the measurements. CONCLUSIONS The DCNN-based CAD system might offer a clinically acceptable diagnostic evaluation of facial asymmetry on PA cephalograms.
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Affiliation(s)
| | - Seojeong Kim
- Korea Electronics Technology Institute, Seongnam, Korea
| | - Kyungmin Clara Lee
- Department of Orthodontics, School of Dentistry, Chonnam National University, Gwangju, Korea
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25
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Kılıç B, İbrahim AH, Aksoy S, Sakman MC, Demircan GS, Önal-Süzek T. A family-centered orthodontic screening approach using a machine learning-based mobile application. J Dent Sci 2024; 19:186-195. [PMID: 38303845 PMCID: PMC10829551 DOI: 10.1016/j.jds.2023.05.001] [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: 04/09/2023] [Revised: 05/01/2023] [Indexed: 02/03/2024] Open
Abstract
Background/purpose Skeletal orthodontic deformities can have functional and aesthetic consequences, making early detection critical. This study aimed to address the issue of parents bringing their children for routine orthodontic checkups after the ideal treatment age has passed. To address this, we developed a mobile application that uses machine-learning to make a preliminary diagnosis of skeletal malocclusion using just one photograph. Materials and methods A retrospective study was conducted on 524 pre-pubertal children, aged between 5 and 12 years, to evaluate the accuracy of the machine learning based mobile application. The application detects multiple points in photographs taken from the mobile camera and generates a signal indicating the diagnosis of skeletal malocclusion. Results The final accuracy of the Class III vs not Class III model deployed to the mobile application was above 81%, indicating its ability to accurately identify skeletal malocclusion. On a separate validation dataset of 145 patients diagnosed by 5 different clinicians, the accuracy of Class II vs Class I model was 69%; And pg 4, ln 61: as Class II vs Class I with 69% accuracy. Conclusion The application provides parents with important information about the orthodontic problem, age of treatment, and various treatment options. This enables parents to seek further advice from an orthodontist at an earlier stage and make informed decisions. However, the diagnosis should still be confirmed by an orthodontist. This approach has the potential to improve access to orthodontic care, especially in underserved communities.
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Affiliation(s)
- Banu Kılıç
- Bezmialem Vakif University, Istanbul, Turkey
| | | | | | - Mehmet Cihan Sakman
- Muğla Sıtkı Koçman University, Muğla, Turkey
- Zurich University of Applied Sciences, Zurich, Switzerland
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26
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Kim IH, Kim JS, Jeong J, Park JW, Park K, Cho JH, Hong M, Kang KH, Kim M, Kim SJ, Kim YJ, Sung SJ, Kim YH, Lim SH, Baek SH, Kim N. Orthognathic surgical planning using graph CNN with dual embedding module: External validations with multi-hospital datasets. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107853. [PMID: 37857025 DOI: 10.1016/j.cmpb.2023.107853] [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: 05/02/2023] [Revised: 09/30/2023] [Accepted: 10/07/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Despite recent development of AI, prediction of the surgical movement in the maxilla and mandible by OGS might be more difficult than that of tooth movement by orthodontic treatment. To evaluate the prediction accuracy of the surgical movement using pairs of pre-(T0) and post-surgical (T1) lateral cephalograms (lat-ceph) of orthognathic surgery (OGS) patients and dual embedding module-graph convolution neural network (DEM-GCNN) model. METHODS 599 pairs from 3 institutions were used as training, internal validation, and internal test sets and 201 pairs from other 6 institutions were used as external test set. DEM-GCNN model (IEM, learning the lat-ceph images; LTEM, learning the landmarks) was developed to predict the amount and direction of surgical movement of ANS and PNS in the maxilla and B-point and Md1crown in the mandible. The distance between T1 landmark coordinates actually moved by OGS (ground truth) and predicted by DEM-GCNN model and pre-existed CNN-based Model-C (learning the lat-ceph images) was compared. RESULTS In both internal and external tests, DEM-GCNN did not exhibit significant difference from ground truth in all landmarks (ANS, PNS, B-point, Md1crown, all P > 0.05). When the accumulated successful detection rate for each landmark was compared, DEM-GCNN showed higher values than Model-C in both the internal and external tests. In violin plots exhibiting the error distribution of the prediction results, both internal and external tests showed that DEM-GCNN had significant performance improvement in PNS, ANS, B-point, Md1crown than Model-C. DEM-GCNN showed significantly lower prediction error values than Model-C (one-jaw surgery, B-point, Md1crown, all P < 0.005; two-jaw surgery, PNS, ANS, all P < 0.05; B point, Md1crown, all P < 0.005). CONCLUSION We developed a robust OGS planning model with maximized generalizability despite diverse qualities of lat-cephs from 9 institutions.
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Affiliation(s)
- In-Hwan Kim
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jun-Sik Kim
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jiheon Jeong
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jae-Woo Park
- Department of Convergence Medicine, University of Ulsan, College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea
| | - Kanggil Park
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jin-Hyoung Cho
- Department of Orthodontics, Chonnam National University School of Dentistry, Gwangju, South Korea
| | - Mihee Hong
- Department of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, South Korea
| | - Kyung-Hwa Kang
- Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan-si, South Korea
| | - Minji Kim
- Department of Orthodontics, College of Medicine, Ewha Womans University, Seoul, South Korea
| | - Su-Jung Kim
- Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, South Korea
| | - Yoon-Ji Kim
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Sang-Jin Sung
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young Ho Kim
- Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon-si, Gyeonggi-do, South Korea
| | - Sung-Hoon Lim
- Department of Orthodontics, College of Dentistry, Chosun University, Gwangju, South Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Daehak-ro 101, Jongno-gu, Seoul 03080, South Korea
| | - Namkug Kim
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Yu JH, Kim JH, Liu J, Mangal U, Ahn HK, Cha JY. Reliability and time-based efficiency of artificial intelligence-based automatic digital model analysis system. Eur J Orthod 2023; 45:712-721. [PMID: 37418746 DOI: 10.1093/ejo/cjad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
OBJECTIVES To compare the reliability, reproducibility, and time-based efficiency of automatic digital (AD) and manual digital (MD) model analyses using intraoral scan models. MATERIAL AND METHODS Two examiners analysed 26 intraoral scanner records using MD and AD methods for orthodontic modelling. Tooth size reproducibility was confirmed using a Bland-Altman plot. The Wilcoxon signed-rank test was conducted to compare the model analysis parameters (tooth size, sum of 12-teeth, Bolton analysis, arch width, arch perimeter, arch length discrepancy, and overjet/overbite) for each method, including the time taken for model analysis. RESULTS The MD group exhibited a relatively larger spread of 95% agreement limits when compared with AD group. The standard deviations of repeated tooth measurements were 0.15 mm (MD group) and 0.08 mm (AD group). The mean difference values of the 12-tooth (1.80-2.38 mm) and arch perimeter (1.42-3.23 mm) for AD group was significantly (P < 0.001) larger than that for the MD group. The arch width, Bolton, and overjet/overbite were clinically insignificant. The overall mean time required for the measurements was 8.62 min and 0.56 min for the MD and AD groups, respectively. LIMITATIONS Validation results may vary in different clinical cases because our evaluation was limited to mild-to-moderate crowding in the complete dentition. CONCLUSIONS Significant differences were observed between AD and MD groups. The AD method demonstrated reproducible analysis in a considerably reduced timeframe, along with a significant difference in measurements compared to the MD method. Therefore, AD analysis should not be interchanged with MD, and vice versa.
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Affiliation(s)
- Jae-Hun Yu
- Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea
- BK21 FOUR Project, Yonsei University College of Dentistry, Seoul, Korea
| | - Ji-Hoi Kim
- Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea
- BK21 FOUR Project, Yonsei University College of Dentistry, Seoul, Korea
| | - Jing Liu
- Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea
| | - Utkarsh Mangal
- Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea
| | - Hee-Kap Ahn
- Department of Computer Science and Engineering, Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Republic of Korea
| | - Jung-Yul Cha
- Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea
- BK21 FOUR Project, Yonsei University College of Dentistry, Seoul, Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea
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28
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Dolci C, Cenzato N, Maspero C, Giannini L, Khijmatgar S, Dipalma G, Tartaglia GM, Inchingolo F. Skull Biomechanics and Simplified Cephalometric Lines for the Estimation of Muscular Lines of Action. J Pers Med 2023; 13:1569. [PMID: 38003884 PMCID: PMC10672339 DOI: 10.3390/jpm13111569] [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: 09/12/2023] [Revised: 10/25/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
Our study introduces a novel cephalometric analysis aimed at facilitating biomechanical simulations by elucidating the intricate relationship between craniofacial morphology and the size and inclination of the masseter muscle (MM) while incorporating muscle values. Our study analyzes the line of action of the MM drawn between the Gonion (Go) and Orbital (Or) points concerning dental and skeletal references (occlusal and Frankfort planes). A total of 510 pre-treatment lateral cephalometric tracings (217 males, 293 females, aged 6-50 years) and lateral Bolton standard tracings were examined. The key parameters investigated include (a) skeletal-cutaneous class (linear distance between projections of points A' and B' on the occlusal plane), (b) the angle between the perpendicular line to the occlusal plane and the Go-Or line at the molar occlusal point, and (c) the angle between the Go-Or line and the Frankfort plane. The assessment of anterior-posterior jaw discrepancy, measured as the skeletal-cutaneous class, ranged from -14.5 to 15.5 mm. Abnormal values were identified in two adolescents, showing no gender- or age-related patterns. The angle between the MM's line of action (Go-Or) and the normal to the occlusal plane averaged 39.3°, while the angle between Go-Or and Po-Or (Frankfort plane) averaged 41.99°. Age had an impact on these angles, with an average 3° decrease in adults and a 4° increase between ages 6 and 50. A weak relationship was observed between sagittal jaw discrepancy and the angle between Go-Or and the Frankfort plane, with about 20% of the variance explained by the anteroposterior maxillary-mandibular relationship. In conclusion, the study presents a cephalometric analysis of the relationship between craniofacial morphology and masseter muscle parameters. It finds that age influences the angles between key reference points, while the skeletal-cutaneous class does not exhibit age- or gender-specific trends. These findings can contribute to a better understanding of craniofacial biomechanics and aid in clinical orthodontic assessments and treatment planning.
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Affiliation(s)
- Claudia Dolci
- Department of Biomedical Sciences for Health, Università Degli Studi di Milano, 20133 Milan, Italy
| | - Niccolò Cenzato
- Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (N.C.)
- Department of Biomedical, Surgical and Dental Sciences, School of Dentistry, Università Degli Studi di Milano, 20100 Milan, Italy
| | - Cinzia Maspero
- Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (N.C.)
- Department of Biomedical, Surgical and Dental Sciences, School of Dentistry, Università Degli Studi di Milano, 20100 Milan, Italy
| | - Lucia Giannini
- Department of Biomedical, Surgical and Dental Sciences, School of Dentistry, Università Degli Studi di Milano, 20100 Milan, Italy
| | - Shahnawaz Khijmatgar
- Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (N.C.)
- Department of Biomedical, Surgical and Dental Sciences, School of Dentistry, Università Degli Studi di Milano, 20100 Milan, Italy
| | - Gianna Dipalma
- Department of Interdisciplinary Medicine, Università Degli Studi di Bari “Aldo Moro”, 70124 Bari, Italy; (G.D.)
| | - Gianluca Martino Tartaglia
- Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy; (N.C.)
- Department of Biomedical, Surgical and Dental Sciences, School of Dentistry, Università Degli Studi di Milano, 20100 Milan, Italy
| | - Francesco Inchingolo
- Department of Interdisciplinary Medicine, Università Degli Studi di Bari “Aldo Moro”, 70124 Bari, Italy; (G.D.)
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Menezes LDS, Silva TP, Lima Dos Santos MA, Hughes MM, Mariano Souza SDR, Leite Ribeiro PM, Freitas PHLD, Takeshita WM. Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software. Dentomaxillofac Radiol 2023; 52:20230065. [PMID: 37869886 DOI: 10.1259/dmfr.20230065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023] Open
Abstract
OBJECTIVES To evaluate the reliability and reproducibility of an artificial intelligence (AI) software in identifying cephalometric points on lateral cephalometric radiographs considering four settings of brightness and contrast. METHODS AND MATERIALS Brightness and contrast of 30 lateral cephalometric radiographs were adjusted into four different settings. Then, the control examiner (ECont), the calibrated examiner (ECal), and the CEFBOT AI software (AIs) each marked 19 cephalometric points on all radiographs. Reliability was assessed with a second analysis of the radiographs 15 days after the first one. Statistical significance was set at p < 0.05. RESULTS Reliability of landmark identification was excellent for the human examiners and the AIs regardless of the type of brightness and contrast setting (mean intraclass correlation coefficient >0.89). When ECont and ECal were compared for reproducibility, there were more cephalometric points with significant differences on the x-axis of the image with the highest contrast and the lowest brightness, namely N(p = 0.033), S(p = 0.030), Po(p < 0.001), and Pog'(p = 0.012). Between ECont and AIs, there were more cephalometric points with significant differences on the image with the highest contrast and the lowest brightness, namely N(p = 0.034), Or(p = 0.048), Po(p < 0.001), A(p = 0.042), Pog'(p = 0.004), Ll(p = 0.005), Ul(p < 0.001), and Sn(p = 0.001). CONCLUSIONS While the reliability of the AIs for cephalometric landmark identification was rated as excellent, low brightness and high contrast seemed to affect its reproducibility. The experienced human examiner, on the other hand, did not show such faulty reproducibility; therefore, the AIs used in this study is an excellent auxiliary tool for cephalometric analysis, but still depends on human supervision to be clinically reliable.
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Affiliation(s)
| | - Thaísa Pinheiro Silva
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, São Paulo, Brazil
| | | | | | | | | | | | - Wilton Mitsunari Takeshita
- Diagnosis and Surgery, São Paulo State University (Unesp), School of Dentistry, Araçatuba, São Paulo, Brazil
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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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Bağ İ, Bilgir E, Bayrakdar İŞ, Baydar O, Atak FM, Çelik Ö, Orhan K. An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population. BMC Oral Health 2023; 23:764. [PMID: 37848870 PMCID: PMC10583406 DOI: 10.1186/s12903-023-03532-8] [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: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. METHODS A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. RESULTS A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). CONCLUSIONS The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.
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Affiliation(s)
- İrem Bağ
- Department of Pediatric Dentistry, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Oğuzhan Baydar
- Dentomaxillofacial Radiology Specialist, Faculty of Dentistry, Ege University, İzmir, Turkey
| | - Fatih Mehmet Atak
- Department of Computer Engineering, The Faculty of Engineering, Boğaziçi University, İstanbul, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Fawaz P, Sayegh PE, Vannet BV. What is the current state of artificial intelligence applications in dentistry and orthodontics? JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2023; 124:101524. [PMID: 37270174 DOI: 10.1016/j.jormas.2023.101524] [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: 03/22/2023] [Revised: 05/08/2023] [Accepted: 05/31/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from. TYPES OF STUDIES REVIEWED Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies. RESULTS The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable. PRACTICAL IMPLICATIONS AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.
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Affiliation(s)
- Paul Fawaz
- Academic Lecturer & Researcher at the Orthodontic department Université de Lorraine, Nancy, France.
| | | | - Bart Vande Vannet
- Clinical and Academical responsable of the Orthodontic department at Université de Lorraine, Nancy, France.
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Jiang C, Jiang F, Xie Z, Sun J, Sun Y, Zhang M, Zhou J, Feng Q, Zhang G, Xing K, Mei H, Li J. Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques. Ann Anat 2023; 250:152114. [PMID: 37302431 DOI: 10.1016/j.aanat.2023.152114] [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: 03/14/2023] [Revised: 05/13/2023] [Accepted: 05/20/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head position, which reduces the accuracy of cephalometric measurements, can be challenging to detect for clinicians. This non-interventional retrospective study aims to develop two deep learning (DL) systems to efficiently, accurately, and instantly detect the head position on LCRs. METHODS LCRs from 13 centers were reviewed and a total of 3000 radiographs were collected and divided into 2400 cases (80.0 %) in the training set and 600 cases (20.0 %) in the validation set. Another 300 cases were selected independently as the test set. All the images were evaluated and landmarked by two board-certified orthodontists as references. The head position of the LCR was classified by the angle between the Frankfort Horizontal (FH) plane and the true horizontal (HOR) plane, and a value within - 3°- 3° was considered normal. The YOLOv3 model based on the traditional fixed-point method and the modified ResNet50 model featuring a non-linear mapping residual network were constructed and evaluated. Heatmap was generated to visualize the performances. RESULTS The modified ResNet50 model showed a superior classification accuracy of 96.0 %, higher than 93.5 % of the YOLOv3 model. The sensitivity&recall and specificity of the modified ResNet50 model were 0.959, 0.969, and those of the YOLOv3 model were 0.846, 0.916. The area under the curve (AUC) values of the modified ResNet50 and the YOLOv3 model were 0.985 ± 0.04 and 0.942 ± 0.042, respectively. Saliency maps demonstrated that the modified ResNet50 model considered the alignment of cervical vertebras, not just the periorbital and perinasal areas, as the YOLOv3 model did. CONCLUSIONS The modified ResNet50 model outperformed the YOLOv3 model in classifying head position on LCRs and showed promising potential in facilitating making accurate diagnoses and optimal treatment plans.
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Affiliation(s)
- Chen Jiang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Fulin Jiang
- Chongqing University Three Gorges Hospital, Chongqing 404031, China
| | - Zhuokai Xie
- University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jikui Sun
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yan Sun
- University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mei Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Jiawei Zhou
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Qingchen Feng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Guanning Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Ke Xing
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Hongxiang Mei
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Juan Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, 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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Indermun S, Shaik S, Nyirenda C, Johannes K, Mulder R. Human examination and artificial intelligence in cephalometric landmark detection-is AI ready to take over? Dentomaxillofac Radiol 2023; 52:20220362. [PMID: 37427581 PMCID: PMC10461256 DOI: 10.1259/dmfr.20220362] [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: 10/28/2022] [Revised: 04/15/2023] [Accepted: 05/07/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVES To compare the precision of two cephalometric landmark identification methods, namely a computer-assisted human examination software and an artificial intelligence program, based on South African data. METHODS This retrospective quantitative cross-sectional analytical study utilized a data set consisting of 409 cephalograms obtained from a South African population. 19 landmarks were identified in each of the 409 cephalograms by the primary researcher using the two programs [(409 cephalograms x 19 landmarks) x 2 methods = 15,542 landmarks)]. Each landmark generated two coordinate values (x, y), making a total of 31,084 landmarks. Euclidean distances between corresponding pairs of observations was calculated. Precision was determined by using the standard deviation and standard error of the mean. RESULTS The primary researcher acted as the gold-standard and was calibrated prior to data collection. The inter- and intrareliability tests yielded acceptable results. Variations were present in several landmarks between the two approaches; however, they were statistically insignificant. The computer-assisted examination software was very sensitive to several variables. Several incidental findings were also discovered. Attempts were made to draw valid comparisons and conclusions. CONCLUSIONS There was no significant difference between the two programs regarding the precision of landmark detection. The present study provides a basis to: (1) support the use of automatic landmark detection to be within the range of computer-assisted examination software and (2) determine the learning data required to develop AI systems within an African context.
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Affiliation(s)
- Suvarna Indermun
- Department of Craniofacial Biology, Pathology and Radiology, University of the Western Cape, Cape Town, South Africa
| | - Shoayeb Shaik
- Department of Oral and Maxillofacial Pathology, Oral Biology and Diagnostic Imaging, University of Pretoria, Pretoria, South Africa
| | - Clement Nyirenda
- Department of Computer Science, University of the Western Cape, Cape Town, South Africa
| | - Keith Johannes
- Department of Orthodontics, University of the Western Cape, Cape Town, South Africa
| | - Riaan Mulder
- Department of Prosthodontics, University of the Western Cape, Cape Town, South Africa
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Ramachandran RA, Barão VAR, Ozevin D, Sukotjo C, Srinivasa PP, Mathew M. Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models. TRIBOLOGY INTERNATIONAL 2023; 187:108735. [PMID: 37720691 PMCID: PMC10503681 DOI: 10.1016/j.triboint.2023.108735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.
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Affiliation(s)
| | - Valentim A R Barão
- Department of Prosthodontics and Periodontology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Didem Ozevin
- Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, IL, USA
| | - Cortino Sukotjo
- Department of Restorative Dentistry, College of Dentistry, University of Illinois at Chicago, IL, USA
| | - Pai P Srinivasa
- Department of Mechanical Engineering, NMAM IT, Nitte, Karnataka, India
| | - Mathew Mathew
- Department of Biomedical Engineering, University of Illinois at Chicago, IL, USA
- Department of Restorative Dentistry, College of Dentistry, University of Illinois at Chicago, IL, USA
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Kunz F, Stellzig-Eisenhauer A, Widmaier LM, Zeman F, Boldt J. Assessment of the quality of different commercial providers using artificial intelligence for automated cephalometric analysis compared to human orthodontic experts. J Orofac Orthop 2023:10.1007/s00056-023-00491-1. [PMID: 37642657 DOI: 10.1007/s00056-023-00491-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/28/2023] [Indexed: 08/31/2023]
Abstract
PURPOSE The aim of this investigation was to evaluate the accuracy of various skeletal and dental cephalometric parameters as produced by different commercial providers that make use of artificial intelligence (AI)-assisted automated cephalometric analysis and to compare their quality to a gold standard established by orthodontic experts. METHODS Twelve experienced orthodontic examiners pinpointed 15 radiographic landmarks on a total of 50 cephalometric X‑rays. The landmarks were used to generate 9 parameters for orthodontic treatment planning. The "humans' gold standard" was defined by calculating the median value of all 12 human assessments for each parameter, which in turn served as reference values for comparisons with results given by four different commercial providers of automated cephalometric analyses (DentaliQ.ortho [CellmatiQ GmbH, Hamburg, Germany], WebCeph [AssembleCircle Corp, Seongnam-si, Korea], AudaxCeph [Audax d.o.o., Ljubljana, Slovenia], CephX [Orca Dental AI, Herzliya, Israel]). Repeated measures analysis of variances (ANOVAs) were calculated and Bland-Altman plots were generated for comparisons. RESULTS The results of the repeated measures ANOVAs indicated significant differences between the commercial providers' predictions and the humans' gold standard for all nine investigated parameters. However, the pairwise comparisons also demonstrate that there were major differences among the four commercial providers. While there were no significant mean differences between the values of DentaliQ.ortho and the humans' gold standard, the predictions of AudaxCeph showed significant deviations in seven out of nine parameters. Also, the Bland-Altman plots demonstrate that a reduced precision of AI predictions must be expected especially for values attributed to the inclination of the incisors. CONCLUSION Fully automated cephalometric analyses are promising in terms of timesaving and avoidance of individual human errors. At present, however, they should only be used under supervision of experienced clinicians.
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Affiliation(s)
- Felix Kunz
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany.
| | | | - Lisa Marie Widmaier
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany
| | - Florian Zeman
- Centre for Clinical Studies, University Hospital of Regensburg, Regensburg, Germany
| | - Julian Boldt
- Department of Prosthetic Dentistry, University Hospital of Würzburg, Würzburg, Germany
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Wong KF, Lam XY, Jiang Y, Yeung AWK, Lin Y. Artificial intelligence in orthodontics and orthognathic surgery: a bibliometric analysis of the 100 most-cited articles. Head Face Med 2023; 19:38. [PMID: 37612673 PMCID: PMC10463886 DOI: 10.1186/s13005-023-00383-0] [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: 07/25/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND The application of artificial intelligence (AI) in orthodontics and orthognathic surgery has gained significant attention in recent years. However, there is a lack of bibliometric reports that analyze the academic literature in this field to identify publishing and citation trends. By conducting an analysis of the top 100 most-cited articles on AI in orthodontics and orthognathic surgery, we aim to unveil popular research topics, key authors, institutions, countries, and journals in this area. METHODS A comprehensive search was conducted in the Web of Science (WOS) electronic database to identify the top 100 most-cited articles on AI in orthodontics and orthognathic surgery. Publication and citation data were obtained and further analyzed and visualized using R Biblioshiny. The key domains of the 100 articles were also identified. RESULTS The top 100 most-cited articles were published between 2005 and 2022, contributed by 458 authors, with an average citation count of 22.09. South Korea emerged as the leading contributor with the highest number of publications (28) and citations (595), followed by China (16, 373), and the United States (7, 248). Notably, six South Korean authors ranked among the top 10 contributors, and three South Korean institutions were listed as the most productive. International collaborations were predominantly observed between the United States, China, and South Korea. The main domains of the articles focused on automated imaging assessment (42%), aiding diagnosis and treatment planning (34%), and the assessment of growth and development (10%). Besides, a positive correlation was observed between the testing sample size and citation counts (P = 0.010), as well as between the time of publication and citation counts (P < 0.001). CONCLUSIONS The utilization of AI in orthodontics and orthognathic surgery has shown remarkable progress, particularly in the domains of imaging analysis, diagnosis and treatment planning, and growth and development assessment. This bibliometric analysis provides valuable insights into the top-cited articles and the trends of AI research in this field.
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Affiliation(s)
- Ka Fai Wong
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China
| | - Xiang Yao Lam
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China
| | - Yuhao Jiang
- Department of Restorative Dentistry, Faculty of Dentistry, the National University of Malaysia, Kuala Lumpur, Malaysia
| | - Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Yifan Lin
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China.
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Kiełczykowski M, Kamiński K, Perkowski K, Zadurska M, Czochrowska E. Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review. Diagnostics (Basel) 2023; 13:2640. [PMID: 37627899 PMCID: PMC10453867 DOI: 10.3390/diagnostics13162640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
In recent years, the application of artificial intelligence (AI) has become more and more widespread in medicine and dentistry. It may contribute to improved quality of health care as diagnostic methods are getting more accurate and diagnostic errors are rarer in daily medical practice. The aim of this paper was to present data from the literature on the effectiveness of AI in orthodontic diagnostics based on the analysis of lateral cephalometric radiographs. A review of the literature from 2009 to 2023 has been performed using PubMed, Medline, Scopus and Dentistry & Oral Sciences Source databases. The accuracy of determining cephalometric landmarks using widely available commercial AI-based software and advanced AI algorithms was presented and discussed. Most AI algorithms used for the automated positioning of landmarks on cephalometric radiographs had relatively high accuracy. At the same time, the effectiveness of using AI in cephalometry varies depending on the algorithm or the application type, which has to be accounted for during the interpretation of the results. In conclusion, artificial intelligence is a promising tool that facilitates the identification of cephalometric landmarks in everyday clinical practice, may support orthodontic treatment planning for less experienced clinicians and shorten radiological examination in orthodontics. In the future, AI algorithms used for the automated localisation of cephalometric landmarks may be more accurate than manual analysis.
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Affiliation(s)
| | | | | | | | - Ewa Czochrowska
- Department of Orthodontics, Medical University in Warsaw, 02-097 Warsaw, Poland; (M.K.); (K.K.); (K.P.); (M.Z.)
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Ye H, Cheng Z, Ungvijanpunya N, Chen W, Cao L, Gou Y. Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification? BMC Oral Health 2023; 23:467. [PMID: 37422630 PMCID: PMC10329795 DOI: 10.1186/s12903-023-03188-4] [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: 01/24/2023] [Accepted: 06/29/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND To evaluate the techniques used for the automatic digitization of cephalograms using artificial intelligence algorithms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in localizing each cephalometric point. METHODS Lateral cephalograms were digitized and traced by three calibrated senior orthodontic residents with or without artificial intelligence (AI) assistance. The same radiographs of 43 patients were uploaded to AI-based machine learning programs MyOrthoX, Angelalign, and Digident. Image J was used to extract x- and y-coordinates for 32 cephalometric points: 11 soft tissue landmarks and 21 hard tissue landmarks. The mean radical errors (MRE) were assessed radical to the threshold of 1.0 mm,1.5 mm, and 2 mm to compare the successful detection rate (SDR). One-way ANOVA analysis at a significance level of P < .05 was used to compare MRE and SDR. The SPSS (IBM-vs. 27.0) and PRISM (GraphPad-vs.8.0.2) software were used for the data analysis. RESULTS Experimental results showed that three methods were able to achieve detection rates greater than 85% using the 2 mm precision threshold, which is the acceptable range in clinical practice. The Angelalign group even achieved a detection rate greater than 78.08% using the 1.0 mm threshold. A marked difference in time was found between the AI-assisted group and the manual group due to heterogeneity in the performance of techniques to detect the same landmark. CONCLUSIONS AI assistance may increase efficiency without compromising accuracy with cephalometric tracings in routine clinical practice and research settings.
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Affiliation(s)
- Huayu Ye
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
| | - Zixuan Cheng
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Haochi Private Dental Clinic, No. 711, Konggang Avenue, Yubei District, Chongqing, 401147 PR China
| | - Nicha Ungvijanpunya
- Faculty of Dentistry, Chulalongkorn University, 34 Henri Dunant Road, Pathumwan, Bangkok, 10330 Thailand
| | - Wenjing Chen
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
| | - Li Cao
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
| | - Yongchao Gou
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
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Chen J, Che H, Sun J, Rao Y, Wu J. An automatic cephalometric landmark detection method based on heatmap regression and Monte Carlo dropout. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083204 DOI: 10.1109/embc40787.2023.10341102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Cephalometric analysis plays an important role in orthodontic diagnosis and treatment planning. It depends on the detection of multiple landmarks, while the process is time-consuming and tedious. Although some deep learning-based automatic landmark detection algorithms have achieved excellent performance, most of them adopt multi-stage models increasing the complexity and detection time. Meanwhile, few studies focused on the uncertainty of detection results, thereby ignoring its significant clinical value. In this paper, we propose a novel approach based on heatmap regression for landmark detection, which can achieve competitive accuracy and good robustness with only one step. Furthermore, by applying Monte Carlo dropout to a U-shaped convolutional neural network, we can obtain not only the coordinate of each landmark but also the corresponding simple uncertainty, so that doctors can pay more attention to those landmarks with higher uncertainty. The evaluation results showed the mean radial error is 1.39±1.06mm and the average successful detection rate is 79.65%, 97.22% within 2mm, 4mm for the IEEE ISBI2015 Test Dataset 1, the indicators for the IEEE ISBI2015 Test Dataset 2 are 1.33±0.93mm, 80.05% and 97.53%, respectively. Our method has the potential to become an assistant tool in clinical practice. Automatic and accurate detection with uncertainty analysis is expected to help guide the doctor's judgment.
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Rauniyar S, Jena S, Sahoo N, Mohanty P, Dash BP. Artificial Intelligence and Machine Learning for Automated Cephalometric Landmark Identification: A Meta-Analysis Previewed by a Systematic Review. Cureus 2023; 15:e40934. [PMID: 37496553 PMCID: PMC10368300 DOI: 10.7759/cureus.40934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2023] [Indexed: 07/28/2023] Open
Abstract
Digital dentistry has become an integral part of our practice today, with artificial intelligence (AI) playing the predominant role. The present systematic review was intended to detect the accuracy of landmarks identified cephalometrically using machine learning and artificial intelligence and compare the same with the manual tracing (MT) group. According to the PRISMA-DTA guidelines, a scoping evaluation of the articles was performed. Electronic databases like Doaj, PubMed, Scopus, Google Scholar, and Embase from January 2001 to November 2022 were searched. Inclusion and exclusion criteria were applied, and 13 articles were studied in detail. Six full-text articles were further excluded (three articles did not provide a comparison between manual tracing and AI for cephalometric landmark detection, and three full-text articles were systematic reviews and meta-analyses). Finally, seven articles were found appropriate to be included in this review. The outcome of this systematic review has led to the conclusion that AI, when employed for cephalometric landmark detection, has shown extremely positive and promising results as compared to manual tracing.
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Affiliation(s)
- Sabita Rauniyar
- Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Science, Bhubaneswar, IND
| | - Sanghamitra Jena
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
| | - Nivedita Sahoo
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
| | - Pritam Mohanty
- Department of Orthodontics, Kalinga Institute of Dental Sciences, Odisha, IND
| | - Bhagabati P Dash
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
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Zhao C, Yuan Z, Luo S, Wang W, Ren Z, Yao X, Wu T. Automatic recognition of cephalometric landmarks via multi-scale sampling strategy. Heliyon 2023; 9:e17459. [PMID: 37416642 PMCID: PMC10320076 DOI: 10.1016/j.heliyon.2023.e17459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023] Open
Abstract
The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.
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Affiliation(s)
- Congyi Zhao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zengbei Yuan
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Shichang Luo
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Wenjie Wang
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zhe Ren
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
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Yuce F, Öziç MÜ, Tassoker M. Detection of pulpal calcifications on bite-wing radiographs using deep learning. Clin Oral Investig 2023; 27:2679-2689. [PMID: 36564651 DOI: 10.1007/s00784-022-04839-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/21/2022] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Pulpal calcifications are discrete hard calcified masses of varying sizes in the dental pulp cavity. This study is aimed at measuring the performance of the YOLOv4 deep learning algorithm to automatically determine whether there is calcification in the pulp chambers in bite-wing radiographs. MATERIALS AND METHODS In this study, 2000 bite-wing radiographs were collected from the faculty database. The oral radiologists labeled the pulp chambers on the radiographs as "Present" and "Absent" according to whether there was calcification. The data were randomly divided into 80% training, 10% validation, and 10% testing. The weight file for pulpal calcification was obtained by training the YOLOv4 algorithm with the transfer learning method. Using the weights obtained, pulp chambers and calcifications were automatically detected on the test radiographs that the algorithm had never seen. Two oral radiologists evaluated the test results, and performance criteria were calculated. RESULTS The results obtained on the test data were evaluated in two stages: detection of pulp chambers and detection of pulpal calcification. The detection performance of pulp chambers was as follows: recall 86.98%, precision 98.94%, F1-score 91.60%, and accuracy 86.18%. Pulpal calcification "Absent" and "Present" detection performance was as follows: recall 86.39%, precision 85.23%, specificity 97.94%, F1-score 85.49%, and accuracy 96.54%. CONCLUSION The YOLOv4 algorithm trained with bite-wing radiographs detected pulp chambers and calcification with high success rates. CLINICAL RELEVANCE Automatic detection of pulpal calcifications with deep learning will be used in clinical practice as a decision support system with high accuracy rates in diagnosing dentists.
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Affiliation(s)
- Fatma Yuce
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Okan University, Istanbul, Turkey
| | - Muhammet Üsame Öziç
- Faculty of Technology Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey
| | - Melek Tassoker
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey.
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Popova T, Stocker T, Khazaei Y, Malenova Y, Wichelhaus A, Sabbagh H. Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network. BMC Oral Health 2023; 23:274. [PMID: 37165409 PMCID: PMC10173502 DOI: 10.1186/s12903-023-02984-2] [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: 11/27/2022] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks. METHODS For the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05. RESULTS The study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances. CONCLUSIONS The results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.
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Affiliation(s)
- Teodora Popova
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Thomas Stocker
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Yeganeh Khazaei
- Department of Statistics, Statistical Consultation Unit, StaBLab, LMU Munich, Akademiestr. 1, 80799, Munich, Germany
| | - Yoana Malenova
- Department of Oral and Maxillofacial Surgery, University Hospital, LMU Munich, Lindwurmstrasse 2a, 80337, Munich, Germany
| | - Andrea Wichelhaus
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Hisham Sabbagh
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
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Rajaram Mohan K, Mathew Fenn S. Artificial Intelligence and Its Theranostic Applications in Dentistry. Cureus 2023; 15:e38711. [PMID: 37292569 PMCID: PMC10246515 DOI: 10.7759/cureus.38711] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 06/10/2023] Open
Abstract
As new technologies emerge, they continue to have an impact on our daily lives, and artificial intelligence (AI) covers a wide range of applications. Because of the advancements in AI, it is now possible to analyse large amounts of data, which results in more accurate data and more effective decision-making. This article explains the fundamentals of AI and examines its development and present use. AI technology has had an impact on the healthcare sector as a result of the need for accurate diagnosis and improved patient care. An overview of the existing AI applications in clinical dentistry was provided. Comprehensive care involving artificial intelligence aims to provide cutting-edge research and innovations, as well as high-quality patient care, by enabling sophisticated decision support tools. The cornerstone of AI advancement in dentistry is creative inter-professional coordination among medical professionals, scientists, and engineers. Artificial intelligence will continue to be associated with dentistry from a wide angle despite potential misconceptions and worries about patient privacy. This is because precise treatment methods and quick data sharing are both essential in dentistry. Additionally, these developments will make it possible for patients, academicians, and healthcare professionals to exchange large data on health as well as provide insights that enhance patient care.
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Affiliation(s)
- Karthik Rajaram Mohan
- Oral Medicine, Vinayaka Mission's Sankarachariyar Dental College, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, IND
| | - Saramma Mathew Fenn
- Oral Medicine and Radiology, Vinayaka Mission's Sankarachariyar Dental College, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, IND
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Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 2023; 23:191. [PMID: 37005593 DOI: 10.1186/s12903-023-02881-8.pmid:37005593;pmcid:pmc10067288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/14/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. METHODS Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. RESULTS The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. CONCLUSION Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.
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Affiliation(s)
- Han Bao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Kejia Zhang
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Chenhao Yu
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Hu Li
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Dan Cao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China
- Centre de Recherche en Information Biomédicale Sino-Français, Rennes, 35000, France
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China
| | - Luwei Liu
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
| | - Bin Yan
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
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Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 2023; 23:191. [PMID: 37005593 PMCID: PMC10067288 DOI: 10.1186/s12903-023-02881-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/14/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. METHODS Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. RESULTS The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. CONCLUSION Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.
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Affiliation(s)
- Han Bao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Kejia Zhang
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Chenhao Yu
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Hu Li
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Dan Cao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China
- Centre de Recherche en Information Biomédicale Sino-Français, Rennes, 35000, France
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China
| | - Luwei Liu
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
| | - Bin Yan
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
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Ryu J, Kim YH, Kim TW, Jung SK. Evaluation of artificial intelligence model for crowding categorization and extraction diagnosis using intraoral photographs. Sci Rep 2023; 13:5177. [PMID: 36997621 PMCID: PMC10063582 DOI: 10.1038/s41598-023-32514-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/28/2023] [Indexed: 04/01/2023] Open
Abstract
Determining the severity of dental crowding and the necessity of tooth extraction for orthodontic treatment planning are time-consuming processes and there are no firm criteria. Thus, automated assistance would be useful to clinicians. This study aimed to construct and evaluate artificial intelligence (AI) systems to assist with such treatment planning. A total of 3,136 orthodontic occlusal photographs with annotations by two orthodontists were obtained. Four convolutional neural network (CNN) models, namely ResNet50, ResNet101, VGG16, and VGG19, were adopted for the AI process. Using the intraoral photographs as input, the crowding group and the necessity of tooth extraction were obtained. Arch length discrepancy analysis with AI-detected landmarks was used for crowding categorization. Various statistical and visual analyses were conducted to evaluate the performance. The maxillary and mandibular VGG19 models showed minimum mean errors of 0.84 mm and 1.06 mm for teeth landmark detection, respectively. Analysis of Cohen's weighted kappa coefficient indicated that crowding categorization performance was best in VGG19 (0.73), decreasing in the order of VGG16, ResNet101, and ResNet50. For tooth extraction, the maxillary VGG19 model showed the highest accuracy (0.922) and AUC (0.961). By utilizing deep learning with orthodontic photographs, dental crowding categorization and diagnosis of orthodontic extraction were successfully determined. This suggests that AI can assist clinicians in the diagnosis and decision making of treatment plans.
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Affiliation(s)
- Jiho Ryu
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Ye-Hyun Kim
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Tae-Woo Kim
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Seok-Ki Jung
- Department of Orthodontics, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea.
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Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry—A review. FRONTIERS IN DENTAL MEDICINE 2023. [DOI: 10.3389/fdmed.2023.1085251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it has not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have been started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry go to the diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for the decision-making of dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review narrated the history and classification of AI, summarised AI applications in dentistry, discussed the relationship between EBD and ML, and aimed to help dental professionals to understand AI as a tool better to assist their routine work with improved efficiency.
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