1
|
Nordblom N, Büttner M, Schwendicke F. Artificial Intelligence in Orthodontics: Critical Review. J Dent Res 2024; 103:577-584. [PMID: 38682436 PMCID: PMC11118788 DOI: 10.1177/00220345241235606] [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: 05/01/2024] Open
Abstract
With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.
Collapse
Affiliation(s)
- N.F. Nordblom
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - M. Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - F. Schwendicke
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, Munich, Germany
| |
Collapse
|
2
|
Ye H, Qin H, Tang Y, Ungvijanpunya N, Gou Y. Mapping an intelligent algorithm for predicting female adolescents' cervical vertebrae maturation stage with high recall and accuracy. Prog Orthod 2024; 25:20. [PMID: 38771402 PMCID: PMC11109046 DOI: 10.1186/s40510-024-00523-5] [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: 01/31/2024] [Accepted: 05/10/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUNDS AND OBJECTIVES The present study was designed to define a novel algorithm capable of predicting female adolescents' cervical vertebrae maturation stage with high recall and accuracy. METHODS A total of 560 female cephalograms were collected, and cephalograms with unclear vertebral shapes and deformed scales were removed. 480 films from female adolescents (mean age: 11.5 years; age range: 6-19 years) were used for the model development phase, and 80 subjects were randomly and stratified allocated to the validation cohort to further assess the model's performance. Derived significant predictive parameters from 15 anatomic points and 25 quantitative parameters of the second to fourth cervical vertebrae (C2-C4) to establish the ordinary logistic regression model. Evaluation metrics including precision, recall, and F1 score are employed to assess the efficacy of the models in each identified cervical vertebrae maturation stage (iCS). In cases of confusion and mispredictions, the model underwent modification to improve consistency. RESULTS Four significant parameters, including chronological age, the ratio of D3 to AH3 (D3:AH3), anterosuperior angle of C4 (@4), and distance between C3lp and C4up (C3lp-C4up) were administered into the ordinary regression model. The primary predicting model that implements the novel algorithm was built and the performance evaluation with all stages of 93.96% for accuracy, 93.98% for precision, 93.98% for recall, and 93.95% for F1-score were obtained. Despite the hybrid logistic-based model achieving high accuracy, the unsatisfactory performance of stage estimation was noticed for iCS3 in the primary cohort (89.17%) and validation cohort (85.00%). Through bivariate logistic regression analysis, the posterior height of C4 (PH4) was further selected in the iCS3 to establish a corrected model, thus the evaluation metrics were upgraded to 95.83% and 90.00%, respectively. CONCLUSIONS An unbiased and objective assessment of the cervical vertebrae maturation (CVM) method can function as a decision-support tool, assisting in the evaluation of the optimal timing for treatment in growing adults. Our novel proposed logistic model yielded individual formulas for each specific CVM stage and attained exceptional performance, indicating the capability to function as a benchmark for maturity evaluation in clinical craniofacial orthopedics for Chinese female adolescents.
Collapse
Affiliation(s)
- Huayu Ye
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China
| | - Hongrui Qin
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China
| | - Ying Tang
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China
| | - Nicha Ungvijanpunya
- Faculty of Dentistry, Chulalongkorn University, 34 Henri Dunant Road, Pathumwan, Bangkok, 10330, Thailand
| | - Yongchao Gou
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China.
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China.
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147, P.R. China.
| |
Collapse
|
3
|
Dipalma G, Inchingolo AD, Inchingolo AM, Piras F, Carpentiere V, Garofoli G, Azzollini D, Campanelli M, Paduanelli G, Palermo A, Inchingolo F. Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review. Diagnostics (Basel) 2023; 13:3677. [PMID: 38132261 PMCID: PMC10743240 DOI: 10.3390/diagnostics13243677] [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: 11/15/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions. MATERIALS AND METHODS Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated. RESULTS A final number of 33 studies were included in the review for qualitative analysis. CONCLUSIONS These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.
Collapse
Affiliation(s)
- Gianna Dipalma
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Alessio Danilo Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Angelo Michele Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Fabio Piras
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Vincenzo Carpentiere
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Grazia Garofoli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Daniela Azzollini
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Merigrazia Campanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Gregorio Paduanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Andrea Palermo
- Implant Dentistry College of Medicine and Dentistry Birmingham, University of Birmingham, Birmingham B46BN, UK;
| | - Francesco Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| |
Collapse
|
4
|
Volovic J, Badirli S, Ahmad S, Leavitt L, Mason T, Bhamidipalli SS, Eckert G, Albright D, Turkkahraman H. A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration. Diagnostics (Basel) 2023; 13:2740. [PMID: 37685278 PMCID: PMC10486486 DOI: 10.3390/diagnostics13172740] [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: 06/28/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.
Collapse
Affiliation(s)
- James Volovic
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | | | - Sunna Ahmad
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Landon Leavitt
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Taylor Mason
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Surya Sruthi Bhamidipalli
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.S.B.); (G.E.)
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (S.S.B.); (G.E.)
| | - David Albright
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (J.V.); (S.A.); (D.A.)
| |
Collapse
|
5
|
Zakhar G, Hazime S, Eckert G, Wong A, Badirli S, Turkkahraman H. Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning. Diagnostics (Basel) 2023; 13:2713. [PMID: 37627972 PMCID: PMC10453460 DOI: 10.3390/diagnostics13162713] [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: 07/03/2023] [Revised: 08/11/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
The goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11-6.07 mm to 0.85-2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32-5.28 mm to 1.25-1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions.
Collapse
Affiliation(s)
- Grant Zakhar
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
| | - Samir Hazime
- Indiana University School of Dentistry, Indianapolis, IN 46202, USA;
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Ariel Wong
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
| | | | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
| |
Collapse
|