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Polizzi A, Leonardi R. Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews. J Dent 2024; 146:105056. [PMID: 38729291 DOI: 10.1016/j.jdent.2024.105056] [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: 03/11/2024] [Revised: 04/25/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVES The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) performance in automatic 2D and 3D cephalometric landmark identification. DATA A combination of free text words and MeSH keywords pooled by boolean operators: Automa* AND cephalo* AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "learning"). SOURCES A search strategy without a timeframe setting was conducted on PubMed, Scopus, Web of Science, Cochrane Library and LILACS. STUDY SELECTION The study protocol followed the PRISMA guidelines and the PICO question was formulated according to the aim of the article. The database search led to the selection of 15 articles that were assessed for eligibility in full-text. Finally, 11 systematic reviews met the inclusion criteria and were analyzed according to the risk of bias in systematic reviews (ROBIS) tool. CONCLUSIONS AI was not able to identify the various cephalometric landmarks with the same accuracy. Since most of the included studies' conclusions were based on a wrong 2 mm cut-off difference between the AI automatic landmark location and that allocated by human operators, future research should focus on refining the most powerful architectures to improve the clinical relevance of AI-driven automatic cephalometric analysis. CLINICAL SIGNIFICANCE Despite a progressively improved performance, AI has exceeded the recommended magnitude of error for most cephalometric landmarks. Moreover, AI automatic landmarking on 3D CBCT appeared to be less accurate compared to that on 2D X-rays. To date, AI-driven cephalometric landmarking still requires the final supervision of an experienced orthodontist.
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Affiliation(s)
- Alessandro Polizzi
- Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy.
| | - Rosalia Leonardi
- Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy
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Leavitt L, Volovic J, Steinhauer L, Mason T, Eckert G, Dean JA, Dundar MM, Turkkahraman H. Can we predict orthodontic extraction patterns by using machine learning? Orthod Craniofac Res 2023; 26:552-559. [PMID: 36843547 DOI: 10.1111/ocr.12641] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/09/2023] [Accepted: 02/13/2023] [Indexed: 02/28/2023]
Abstract
OBJECTIVE To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population. MATERIALS AND METHODS The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race/ethnicity and gender. Fifty-five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns. RESULTS The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (<50%) for the upper 1st and lower 2nd premolars (U4/L5s), upper 2nd and lower 1st premolars (U5/L4s), and upper and lower 2nd premolars (U/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%. CONCLUSION All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.
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Affiliation(s)
- Landon Leavitt
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - James Volovic
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Lily Steinhauer
- Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Taylor Mason
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jeffrey A Dean
- Department of Pediatric Dentistry, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - M Murat Dundar
- School of Science, Department of Computer & Information Science, Indiana University Purdue University at Indianapolis, Indianapolis, Indiana, USA
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
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Londono J, Ghasemi S, Hussain Shah A, Fahimipour A, Ghadimi N, Hashemi S, Khurshid Z, Dashti M. Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis. Saudi Dent J 2023; 35:487-497. [PMID: 37520606 PMCID: PMC10373073 DOI: 10.1016/j.sdentj.2023.05.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Cephalometry is the study of skull measurements for clinical evaluation, diagnosis, and surgical planning. Machine learning (ML) algorithms have been used to accurately identify cephalometric landmarks and detect irregularities related to orthodontics and dentistry. ML-based cephalometric imaging reduces errors, improves accuracy, and saves time. Method In this study, we conducted a meta-analysis and systematic review to evaluate the accuracy of ML software for detecting and predicting anatomical landmarks on two-dimensional (2D) lateral cephalometric images. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for selecting and screening research articles. The eligibility criteria were established based on the diagnostic accuracy and prediction of ML combined with 2D lateral cephalometric imagery. The search was conducted among English articles in five databases, and data were managed using Review Manager software (v. 5.0). Quality assessment was performed using the diagnostic accuracy studies (QUADAS-2) tool. Result Summary measurements included the mean departure from the 1-4-mm threshold or the percentage of landmarks identified within this threshold with a 95% confidence interval (CI). This meta-analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. The studies were conducted in various regions of the world, and 20 of the studies employed convolutional neural networks (CNNs) for detecting cephalometric landmarks. The pooled successful detection rates for the 1-mm, 2-mm, 2.5-mm, 3-mm, and 4-mm ranges were 65%, 81%, 86%, 91%, and 96%, respectively. Heterogeneity was determined using the random effect model. Conclusion In conclusion, ML has shown promise for landmark detection in 2D cephalometric imagery, although the accuracy has varied among studies and clinicians. Consequently, more research is required to determine its effectiveness and reliability in clinical settings.
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Affiliation(s)
- Jimmy Londono
- FACP, Professor and Director of the Prosthodontics Residency Program and the Ronald Goldstein Center for Esthetics and Implant Dentistry, Dental College of Georgia at Augusta University, Augusta, GA, United States
| | - Shohreh Ghasemi
- Department of Oral and Maxillofacial Surgery, The Dental College of Georgia at Augusta University, Augusta, GA, United States
| | - Altaf Hussain Shah
- Special Care Dentistry Clinics, University Dental Hospital, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Amir Fahimipour
- School of Dentistry, Faculty of Medicine and Health, Westmead Centre for Oral Health, The University of Sydney, NSW 2145, Australia
| | - Niloofar Ghadimi
- Department of Oral and Maxillofacial Radiology, Dental School, Islamic Azad University of Medical Sciences, Tehran, Iran
| | - Sara Hashemi
- School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zohaib Khurshid
- Department of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
- Center of Excellence for Regenerative Dentistry, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, Thailand
| | - Mahmood Dashti
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Lee H, Ahmad S, Frazier M, Dundar MM, Turkkahraman H. A novel machine learning model for class III surgery decision. J Orofac Orthop 2022:10.1007/s00056-022-00421-7. [PMID: 36018345 DOI: 10.1007/s00056-022-00421-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 07/24/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE The primary purpose of this study was to develop a new machine learning model for the surgery/non-surgery decision in class III patients and evaluate the validity and reliability of this model. METHODS The sample consisted of 196 skeletal class III patients. All the cases were allocated randomly, 136 to the training set and the remaining 60 to the test set. Using the test set, the success rate of the artificial neural network model was estimated, along with a 95% confidence interval. To predict surgical cases, we trained a binary classifier using two different methods: random forest (RF) and logistic regression (LR). RESULTS Both the RF and the LR model showed high separability when classifying each patient for surgical or non-surgical treatment. RF achieved an area under the curve (AUC) of 0.9395 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.7908 and higher bound = 0.9799. On the other hand, LR achieved an AUC of 0.937 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.8467 and higher bound = 0.9812. CONCLUSIONS RF and LR machine learning models can be used to generate accurate and reliable algorithms to successfully classify patients up to 90%. The features selected by the algorithms coincide with the clinical features that we as clinicians weigh heavily when determining a treatment plan. This study further supports that overjet, Wits appraisal, lower incisor angulation, and Holdaway H angle can be used as strong predictors in assessing a patient's surgical needs.
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Affiliation(s)
- Hunter Lee
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, 1121 West Michigan Street, 46202, Indianapolis, IN, USA
| | - Sunna Ahmad
- Indiana University School of Dentistry, Indianapolis, IN, USA
| | - Michael Frazier
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, 1121 West Michigan Street, 46202, Indianapolis, IN, USA
| | - Mehmet Murat Dundar
- Department of Computer and Information Science, School of Science, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, 1121 West Michigan Street, 46202, Indianapolis, IN, USA.
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Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1880113. [PMID: 35757486 PMCID: PMC9225851 DOI: 10.1155/2022/1880113] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 05/13/2022] [Indexed: 11/17/2022]
Abstract
Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic dispensation, machine learning, and planning. In the area of medicine and dentistry, machine learning is currently the most widely used AI application. This narrative review is aimed at giving an outline of cephalometric analysis in orthodontics using AI. Latest algorithms are developing rapidly, and computational resources are increasing, resulting in increased efficiency, accuracy, and reliability. Current techniques for completely automatic identification of cephalometric landmarks have considerably improved efficiency and growth prospects for their regular use. The primary considerations for effective orthodontic treatment are an accurate diagnosis, exceptional treatment planning, and good prognosis estimation. The main objective of the AI technique is to make dentists' work more precise and accurate. AI is increasingly being used in the area of orthodontic treatment. It has been evidenced to be a time-saving and reliable tool in many ways. AI is a promising tool for facilitating cephalometric tracing in routine clinical practice and analyzing large databases for research purposes.
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Auconi P, Gili T, Capuani S, Saccucci M, Caldarelli G, Polimeni A, Di Carlo G. The Validity of Machine Learning Procedures in Orthodontics: What Is Still Missing? J Pers Med 2022; 12:jpm12060957. [PMID: 35743742 PMCID: PMC9225071 DOI: 10.3390/jpm12060957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/31/2022] [Accepted: 06/05/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence (AI) models and procedures hold remarkable predictive efficiency in the medical domain through their ability to discover hidden, non-obvious clinical patterns in data. However, due to the sparsity, noise, and time-dependency of medical data, AI procedures are raising unprecedented issues related to the mismatch between doctors’ mentalreasoning and the statistical answers provided by algorithms. Electronic systems can reproduce or even amplify noise hidden in the data, especially when the diagnosis of the subjects in the training data set is inaccurate or incomplete. In this paper we describe the conditions that need to be met for AI instruments to be truly useful in the orthodontic domain. We report some examples of computational procedures that are capable of extracting orthodontic knowledge through ever deeper patient representation. To have confidence in these procedures, orthodontic practitioners should recognize the benefits, shortcomings, and unintended consequences of AI models, as algorithms that learn from human decisions likewise learn mistakes and biases.
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Affiliation(s)
- Pietro Auconi
- Private Practice of Orthodontics, 00012 Rome, Italy;
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 55100 Lucca, Italy
- ISC CNR, Department of Physics, University of Rome “Sapienza”, P.le Aldo Moro 5, 00185 Rome, Italy; (S.C.); (G.C.)
- Correspondence:
| | - Silvia Capuani
- ISC CNR, Department of Physics, University of Rome “Sapienza”, P.le Aldo Moro 5, 00185 Rome, Italy; (S.C.); (G.C.)
| | - Matteo Saccucci
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, Viale Regina Elena 287a, 00161 Rome, Italy; (M.S.); (A.P.); (G.D.C.)
| | - Guido Caldarelli
- ISC CNR, Department of Physics, University of Rome “Sapienza”, P.le Aldo Moro 5, 00185 Rome, Italy; (S.C.); (G.C.)
- Department of Molecular Sciences and Nanosystems, Ca’Foscari University of Venice, Via Torino 155, Venezia Mestre, 30172 Venice, Italy
- ECLT, Ca’ Bottacin, Dorsoduro 3246, 30123 Venice, Italy
| | - Antonella Polimeni
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, Viale Regina Elena 287a, 00161 Rome, Italy; (M.S.); (A.P.); (G.D.C.)
| | - Gabriele Di Carlo
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, Viale Regina Elena 287a, 00161 Rome, Italy; (M.S.); (A.P.); (G.D.C.)
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Kondody RT, Patil A, Devika G, Jose A, Kumar A, Nair S. Introduction to artificial intelligence and machine learning into orthodontics: A review. APOS TRENDS IN ORTHODONTICS 2022. [DOI: 10.25259/apos_60_2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Over the past few years, artificial intelligence (AI) and machine learning (ML) have revolutionized different healthcare branches, including dentistry. AI in a wider aspect means computers that mimic or behave like human intelligence whereas ML forms a part of AI and enables machines to increase their capabilities by the process of self-adapting algorithms. AI models’ basic principles or fundamentals are purely based on artificial neural networks or convolutional neural networks. This review focuses on giving a comprehensive and detailed explanation about AI and ML technology and their wide range of applications in various sections of orthodontic practice.
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Affiliation(s)
- Rony T. Kondody
- Department of Orthodontics, Sri Rajiv Gandhi College of Dental Science and Hospital, Bengaluru, India,
| | - Aishwarya Patil
- Department of Oral Pathology and microbiology, HKES’s S. Nijalingappa Dental College and Hospital, Gulbarga, India,
| | - G. Devika
- Department of Periodontics, Oxford Dental College and Hospital, Bengaluru, India,
| | - Angeline Jose
- Department of Conservative Dentistry and Endodontics, ESIC Govt. Dental College and Hospital, Gulbarga, Karnataka, India,
| | - Ashwath Kumar
- Department of Conservative Dentistry and Endodontics, ESIC Govt. Dental College and Hospital, Gulbarga, Karnataka, India,
| | - Saumya Nair
- Department of Conservative Dentistry and Endodontics, Annoor Dental College and Hospital, Muvattupuzha, Kerala, India,
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Raveendran R, Perumbure S, Nath SG. Artificial intelligence: A newer vista in dentistry. Artif Organs 2021; 46:1712. [PMID: 34873730 DOI: 10.1111/aor.14128] [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: 11/20/2021] [Accepted: 11/22/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Artificial intelligence (AI) is one of the newest fields in science and engineering. It refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Artificial intelligence as a science is very broad and encompasses various fields, including reasoning, natural language processing, planning, and machine learning. In modern times the real-world current applications of AI include health care, automotive, finance and economics, playing video games, solving mathematical theorems, writing poetry, driving a car on a crowded street, and many more all of which aim to improve human life. METHODS The aim of this article is to review the current application of AI in the field of dentistry based on electronic search in various data bases like Google scholar, PubMed, and Scopus. RESULTS The present review outlines the potential applications of AI in the field of Dentistry in diagnosis, treatment planning, and disease prediction and discusses its impact on dentists, with the objective of creating a support for future research in this rapidly expanding arena. CONCLUSIONS Artificial intelligence systems can simplify the tasks, give a standardization to the procedures and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently.
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Affiliation(s)
- Ranjith Raveendran
- Department of Orthodontics & Dentofacial Orthopedics, Govt. Dental College, Kannur, India
| | - Suresh Perumbure
- Centre for Interdisciplinary Research, Innovation and Entrepreneurship (CIDRIE), Muthoot Institute of Technology and Science, Ernakulam, India
| | - Sameera G Nath
- Department of Periodontics, Govt. Dental College, Kozhikode, India
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Gili T, Di Carlo G, Capuani S, Auconi P, Caldarelli G, Polimeni A. Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics. Orthod Craniofac Res 2021; 24 Suppl 2:16-25. [PMID: 34519158 PMCID: PMC9292769 DOI: 10.1111/ocr.12520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 06/23/2021] [Indexed: 12/19/2022]
Abstract
Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning underlying biomedicine. Complexity theory, Network analysis and Artificial Intelligence are already approaching this discipline, intending to provide support for patient's diagnosis, prognosis and treatments. At the same time, due to the sparsity, noisiness and time-dependency of medical data, such procedures are raising many unprecedented problems related to the mismatch between the human mind's reasoning and the outputs of computational models. Thanks to these computational, non-anthropocentric models, a patient's clinical situation can be elucidated in the orthodontic discipline, and the growth outcome can be approximated. However, to have confidence in these procedures, orthodontists should be warned of the related benefits and risks. Here we want to present how these innovative approaches can derive better patients' characterization, also offering a different point of view about patient's classification, prognosis and treatment.
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Affiliation(s)
- Tommaso Gili
- Networks UnitIMT School for Advanced Studies LuccaLuccaItaly
- CNR‐ISC Unità SapienzaRomeItaly
| | - Gabriele Di Carlo
- Department of Oral and Maxillo‐Facial SciencesSapienza University of RomeRomeItaly
| | | | | | - Guido Caldarelli
- CNR‐ISC Unità SapienzaRomeItaly
- Department of Molecular Sciences and NanosystemsCa’Foscari University of VeniceVenezia MestreItaly
| | - Antonella Polimeni
- Department of Oral and Maxillo‐Facial SciencesSapienza University of RomeRomeItaly
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Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod 2021; 22:18. [PMID: 34219198 PMCID: PMC8255249 DOI: 10.1186/s40510-021-00361-9] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/12/2021] [Indexed: 12/15/2022] Open
Abstract
Introduction This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application. Methods A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020. Results Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning—either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein. Conclusion This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment. Supplementary Information The online version contains supplementary material available at 10.1186/s40510-021-00361-9.
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Affiliation(s)
| | | | | | | | - Carlos Flores-Mir
- Department of Orthodontics, University of Alberta, Edmonton, Alberta, Canada
| | - Nikhilesh R Vaid
- Department of Orthodontics, European University College, Dubai, United Arab Emirates
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11
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Perillo L, Auconi P, d'Apuzzo F, Grassia V, Scazzocchio M, Nucci L, McNamara JA, Franchi L. Machine learning in the prognostic appraisal of Class III growth. Semin Orthod 2021. [DOI: 10.1053/j.sodo.2021.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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12
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Leonardi R, Giudice AL, Isola G, Spampinato C. Deep learning and computer vision: Two promising pillars, powering the future in orthodontics. Semin Orthod 2021. [DOI: 10.1053/j.sodo.2021.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Littlewood SJ, Dalci O, Dolce C, Holliday LS, Naraghi S. Orthodontic retention: what's on the horizon? Br Dent J 2021; 230:760-764. [PMID: 34117435 PMCID: PMC8193167 DOI: 10.1038/s41415-021-2937-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/14/2020] [Indexed: 11/08/2022]
Abstract
Orthodontic retention remains one of the great challenges in orthodontics. In this article, we discuss what is on the horizon to help address this challenge, including biological approaches to reduce relapse, treating patients without using retainers, technological developments, personalised medicine and the impact of COVID-19 on approaches to orthodontic retention.
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Affiliation(s)
- Simon J Littlewood
- Consultant Orthodontist, Department of Orthodontics, St Luke's Hospital, Bradford, UK.
| | - Oyku Dalci
- Senior Lecturer, Discipline of Orthodontics and Paediatric Dentistry, School of Dentistry, Faculty of Medicine and Health, University of Sydney, Australia
| | - Calogero Dolce
- Professor and Chairman, Department of Orthodontics, University of Florida, College of Dentistry, Gainesville, Florida, USA
| | - L Shannon Holliday
- Associate Professor, Department of Orthodontics, University of Florida, College of Dentistry, Gainesville, Florida, USA
| | - Sasan Naraghi
- Consultant Orthodontist, Orthodontic Clinic, Public Dental Health, Växjö, Sweden
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Al Turkestani N, Bianchi J, Deleat-Besson R, Le C, Tengfei L, Prieto JC, Gurgel M, Ruellas ACO, Massaro C, Aliaga Del Castillo A, Evangelista K, Yatabe M, Benavides E, Soki F, Zhang W, Najarian K, Gryak J, Styner M, Fillion-Robin JC, Paniagua B, Soroushmehr R, Cevidanes LHS. Clinical decision support systems in orthodontics: A narrative review of data science approaches. Orthod Craniofac Res 2021; 24 Suppl 2:26-36. [PMID: 33973362 DOI: 10.1111/ocr.12492] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/15/2021] [Accepted: 05/04/2021] [Indexed: 12/27/2022]
Abstract
Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.
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Affiliation(s)
- Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Restorative and Aesthetic Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jonas Bianchi
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, CA, USA
| | - Romain Deleat-Besson
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Celia Le
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Li Tengfei
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Antonio C O Ruellas
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, School of Dentistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Camila Massaro
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, Bauru Dental School, University of São Paulo, São Paulo, Brazil
| | - Aron Aliaga Del Castillo
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, Bauru Dental School, University of São Paulo, São Paulo, Brazil
| | - Karine Evangelista
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.,Department of Orthodontics, School of Dentistry, University of Goias, Goiania, Brazil
| | - Marilia Yatabe
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Erika Benavides
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Fabiana Soki
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, MI, USA
| | - Winston Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Martin Styner
- Departments Psychiatry and Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | | | | | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Lucia H S Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA
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Shetty VG, Rai R, Shetty KN. Artificial intelligence and machine learning: The new paradigm in orthodontic practice. INTERNATIONAL JOURNAL OF ORTHODONTIC REHABILITATION 2020. [DOI: 10.4103/ijor.ijor_35_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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