1
|
Obwegeser D, Timofte R, Mayer C, Bornstein MM, Schätzle MA, Patcas R. Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions. Orthod Craniofac Res 2024. [PMID: 38825845 DOI: 10.1111/ocr.12820] [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: 05/17/2024] [Indexed: 06/04/2024]
Abstract
OBJECTIVE In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions affect facial attractiveness and to explore whether a dedicated training of the CNN is able to reduce the bias of facial expressions. MATERIALS AND METHODS Frontal facial images (n = 840) of 40 female participants (mean age 24.5 years) were taken adapting a neutral facial expression and the six universal facial expressions. Facial attractiveness was computed by means of a face detector, deep convolutional neural networks, standard support vector regression for facial beauty, visual regularized collaborative filtering and a regression technique for handling visual queries without rating history. CNN was first trained on random facial photographs from a dating website and then further trained on the Chicago Face Database (CFD) to increase its suitability to medical conditions. Both algorithms scored every image for attractiveness. RESULTS Facial expressions affect facial attractiveness scores significantly. Scores from CNN additionally trained on CFD had less variability between the expressions (range 54.3-60.9 compared to range: 32.6-49.5) and less variance within the scores (P ≤ .05), but also caused a shift in the ranking of the expressions' facial attractiveness. CONCLUSION Facial expressions confound attractiveness scores. Training on norming images generated scores less susceptible to distortion, but more difficult to interpret. Scoring facial attractiveness based on CNN seems promising, but AI solutions must be developed on CNN trained to recognize facial expressions as distractors.
Collapse
Affiliation(s)
- Dorothea Obwegeser
- Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Radu Timofte
- Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
- CAIDAS and Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Wurzburg, Wurzburg, Germany
| | - Christoph Mayer
- Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Michael M Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Marc A Schätzle
- Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Raphael Patcas
- Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| |
Collapse
|
2
|
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.
Collapse
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.
| |
Collapse
|
3
|
Xing L, Zhang X, Guo Y, Bai D, Xu H. XGBoost-aided prediction of lip prominence based on hard-tissue measurements and demographic characteristics in an Asian population. Am J Orthod Dentofacial Orthop 2023; 164:357-367. [PMID: 36959014 DOI: 10.1016/j.ajodo.2023.01.017] [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: 08/01/2022] [Revised: 01/01/2023] [Accepted: 01/01/2023] [Indexed: 03/25/2023]
Abstract
INTRODUCTION Prediction of lip prominence based on hard-tissue measurements could be helpful in orthodontic treatment planning and has been challenging and formidable thus far. METHODS A machine learning-based cross-sectional study was conducted on 1549 patients. Hard-tissue measurements and demographic information were used as the input features. Seven popular machine learning algorithms were applied to the datasets to predict upper and lower lip prominence. The algorithm that performed the best was selected for the construction of the prediction model. Evaluation of feature importance was conducted using 3 classical methods. RESULTS Among the 7 algorithms, the XGBoost model performed the best in the prediction of the distances between labrale superius or labrale inferius to the esthetics plane (UL-EP and LL-EP distances), with root mean square error values of 1.25, 1.49 and r2 values of 0.755 and 0.683, respectively. Among the 14 input features, the L1-NB distance contributed the most to the prominences of the upper and lower lips. A lip prominence predictor was developed to facilitate clinical application by deploying the prediction model into a downloadable tool kit. CONCLUSIONS The XGBoost model performed well with high accuracy and practicability in predicting upper and lower lip prominence. The artificial intelligence-aided predictor could serve as a reference for orthodontic treatment planning.
Collapse
Affiliation(s)
- Lu Xing
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China
| | - Xiaoqi Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China
| | - Yongwen Guo
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China; Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Ding Bai
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China; Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Hui Xu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, China; Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
| |
Collapse
|
4
|
Ueda A, Tussie C, Kim S, Kuwajima Y, Matsumoto S, Kim G, Satoh K, Nagai S. Classification of Maxillofacial Morphology by Artificial Intelligence Using Cephalometric Analysis Measurements. Diagnostics (Basel) 2023; 13:2134. [PMID: 37443528 DOI: 10.3390/diagnostics13132134] [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: 04/04/2023] [Revised: 06/08/2023] [Accepted: 06/15/2023] [Indexed: 07/15/2023] Open
Abstract
The characteristics of maxillofacial morphology play a major role in orthodontic diagnosis and treatment planning. While Sassouni's classification scheme outlines different categories of maxillofacial morphology, there is no standardized approach to assigning these classifications to patients. This study aimed to create an artificial intelligence (AI) model that uses cephalometric analysis measurements to accurately classify maxillofacial morphology, allowing for the standardization of maxillofacial morphology classification. This study used the initial cephalograms of 220 patients aged 18 years or older. Three orthodontists classified the maxillofacial morphologies of 220 patients using eight measurements as the accurate classification. Using these eight cephalometric measurement points and the subject's gender as input features, a random forest classifier from the Python sci-kit learning package was trained and tested with a k-fold split of five to determine orthodontic classification; distinct models were created for horizontal-only, vertical-only, and combined maxillofacial morphology classification. The accuracy of the combined facial classification was 0.823 ± 0.060; for anteroposterior-only classification, the accuracy was 0.986 ± 0.011; and for the vertical-only classification, the accuracy was 0.850 ± 0.037. ANB angle had the greatest feature importance at 0.3519. The AI model created in this study accurately classified maxillofacial morphology, but it can be further improved with more learning data input.
Collapse
Affiliation(s)
- Akane Ueda
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
- Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
| | - Cami Tussie
- DMD Candidate Class of 2025, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
| | - Sophie Kim
- DMD Candidate Class of 2025, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
| | - Yukinori Kuwajima
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
| | - Shikino Matsumoto
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
| | - Grace Kim
- Department of Developmental Biology, Harvard School of Dental Medicine,188 Longwood Avenue, Boston, MA 02115, USA
| | - Kazuro Satoh
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
| | - Shigemi Nagai
- Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
| |
Collapse
|
5
|
Machine Learning in Dentistry: A Scoping Review. J Clin Med 2023; 12:jcm12030937. [PMID: 36769585 PMCID: PMC9918184 DOI: 10.3390/jcm12030937] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/06/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.
Collapse
|
6
|
Patcas R, Bornstein MM, Schätzle MA, Timofte R. Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges. Clin Oral Investig 2022; 26:6871-6879. [DOI: 10.1007/s00784-022-04724-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 09/14/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Objectives
This review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together with the associated opportunities, obstacles, and ethico-legal concerns.
Material and methods
Narrative literature review.
Results
Narrative literature review.
Conclusion
Curated facial images are widely available and easily accessible and are as such particularly suitable big data for ANN training. New AI solutions have the potential to change contemporary dentistry by optimizing existing processes and enriching dental care with the introduction of new tools for assessment or treatment planning. The analyses of health-related big data may also contribute to revolutionize personalized medicine through the detection of previously unknown associations. In regard to facial images, advances in medico-dental AI-based diagnostics include software solutions for the detection and classification of pathologies, for rating attractiveness and for the prediction of age or gender. In order for an ANN to be suitable for medical diagnostics of the face, the arising challenges regarding computation and management of the software are discussed, with special emphasis on the use of non-medical big data for ANN training. The legal and ethical ramifications of feeding patients’ facial images to a neural network for diagnostic purposes are related to patient consent, data privacy, data security, liability, and intellectual property. Current ethico-legal regulation practices seem incapable of addressing all concerns and ensuring accountability.
Clinical significance
While this review confirms the many benefits derived from AI solutions used for the diagnosis of medical images, it highlights the evident lack of regulatory oversight, the urgent need to establish licensing protocols, and the imperative to investigate the moral quality of new norms set with the implementation of AI applications in medico-dental diagnostics.
Collapse
|
7
|
Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022; 10:healthcare10071269. [PMID: 35885796 PMCID: PMC9320442 DOI: 10.3390/healthcare10071269] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
Collapse
|
8
|
Marya A, Venugopal A, Karobari MI, Chaudhari PK, Heboyan A, Rokaya D. The Contemporary Management of Cleft Lip and Palate and the Role of Artificial Intelligence: A Review. Open Dent J 2022. [DOI: 10.2174/18742106-v16-e2202240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Introduction:
Cleft management is an exhaustive process for the patient, the orthodontist, and the caregiver. In recent decades, a wide number of challenges have been addressed with the inclusion of various dental specialties for the detection, diagnosis, and treatment of orofacial clefts. The orthodontist plays a very pivotal role during the overall management of children with cleft lip and palate as they need to make critical decisions for when to intervene orthodontically and at what stage to set priorities for individual treatment goals.
Objectives:
The objectives of this study were to provide an in-depth review of the evolving role of various disciplines focusing on orthodontics in the management of cleft cases.
Methods:
A general search was carried out to identify the published data on cleft lip and cleft palate management on PubMed and Scopus until the 1st of June 2021 using keywords such as cleft lip, cleft palate, cleft orthodontics, naso-alveolar molding, and surgical cleft orthodontics. The related literature was then reviewed and analyzed.
Results:
With improvements in 3D modeling, CT scans of patients can be used to construct precise 3D models, and these can be utilized to demonstrate various clinical issues related to clefts. The orthodontist has a major role in the various stages and steps, follow-up, treatment care, and outcome assessment. With the advent of technological advancements and artificial intelligence, the role is only going to evolve and expand further in the management of the cleft lip and palate. Diagnostic techniques utilizing artificial intelligence to detect cleft during the prenatal period have also been tested and have been shown to have a high rate of accuracy. The evolution of distraction osteogenesis came into the limelight as a revolutionary modality for cleft treatment. Computer-assisted orthognathic surgery is a widely used modality for reshaping the osseous defects of the maxilla in patients with congenital clefts. With the development of additional modalities such as aligners, patients that need to undergo complex orthognathic surgeries can also be treated with aligners without compromising the outcomes.
Conclusion:
The cleft lip and palate can be managed by a multi-disciplinary team. Orthodontics has an important role in the overall management of a cleft affected individual as they must make critical decisions regarding orthodontic interventions as well as set priorities for each treatment goal. With the advent of technological advancements and artificial intelligence, the diagnosis and management of the cleft lip and palate have become simplified.
Collapse
|
9
|
Zheng S, Chen K, Lin X, Liu S, Han J, Wu G. Quantitative analysis of facial proportions and facial attractiveness among Asians and Caucasians. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:6379-6395. [PMID: 35603407 DOI: 10.3934/mbe.2022299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
It has been proposed that the proportions of the human face are crucial for facial aesthetics. If this is the case, we should describe the relationship among proportions of face components quantitatively. This study aims to develop a mathematical model of facial proportions to provide a quantitative description of facial attractiveness. Furthermore, we expect that plastic surgeons can use models in clinical work to enhance communication efficiency between doctors and patients. Face alignment technique was used to analyse 5500 frontal faces with diverse properties (male/female, Asian/Caucasian, ages) to obtain the ratios among the nose length ($ {N}_{L} $), the nasal base width ($ N $), and the inner canthus width ($ {E}_{I} $). A mathematical model ($ {N}_{L}^{2} = a{E}_{I}\mathrm{*}{N}_{L}+b{E}_{I}\mathrm{*}N+cN\mathrm{*}{N}_{L} $) was developed to describe the relationship among these proportions. To validate the effectiveness of this approach, we simulated the post-operative photos using Adobe Photoshop. Our findings show that the ratio of nose length to nose width, the ratio of inner canthus width to nose length and the ratio of inner canthus to nose width play a significant role in determining facial attractiveness. These results provide a possible strategy to quantitatively describe the relationship among human face proportions.
Collapse
Affiliation(s)
- Shikang Zheng
- Department of Oral, Plastic and Aesthetic Surgery, Hospital of Stomatology, Jilin University, Jilin 130021, China
| | - Kai Chen
- Department of Oral, Plastic and Aesthetic Surgery, Hospital of Stomatology, Jilin University, Jilin 130021, China
| | - Xinping Lin
- Department of Oral, Plastic and Aesthetic Surgery, Hospital of Stomatology, Jilin University, Jilin 130021, China
| | - Shiqian Liu
- College of Mathematics, Jilin University, Jilin 130021, China
| | - Jie Han
- Academy of Marxism, Jilin University, Jilin 130021, China
| | - Guomin Wu
- Department of Oral, Plastic and Aesthetic Surgery, Hospital of Stomatology, Jilin University, Jilin 130021, China
| |
Collapse
|
10
|
Commercial Artificial Intelligence Software as a Tool for Assessing Facial Attractiveness: A Proof-of-Concept Study in an Orthognathic Surgery Cohort. Aesthetic Plast Surg 2022; 46:1013-1016. [PMID: 34494125 DOI: 10.1007/s00266-021-02537-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 08/08/2021] [Indexed: 01/11/2023]
|
11
|
Ossowska A, Kusiak A, Świetlik D. Artificial Intelligence in Dentistry-Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063449. [PMID: 35329136 PMCID: PMC8950565 DOI: 10.3390/ijerph19063449] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022]
Abstract
Nowadays, artificial intelligence (AI) is becoming more important in medicine and in dentistry. It can be helpful in many fields where the human may be assisted and helped by new technologies. Neural networks are a part of artificial intelligence, and are similar to the human brain in their work and can solve given problems and make fast decisions. This review shows that artificial intelligence and the use of neural networks has developed very rapidly in recent years, and it may be an ordinary tool in modern dentistry in the near future. The advantages of this process are better efficiency, accuracy, and time saving during the diagnosis and treatment planning. More research and improvements are needed in the use of neural networks in dentistry to put them into daily practice and to facilitate the work of the dentist.
Collapse
Affiliation(s)
- Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdańsk, 80-204 Gdańsk, Poland;
| | - Aida Kusiak
- Department of Biostatistics and Neural Networks, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
| | - Dariusz Świetlik
- Department of Biostatistics and Neural Networks, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
- Correspondence:
| |
Collapse
|
12
|
Perceived Age and Attractiveness Using Facial Recognition Software in Rhinoplasty Patients. J Craniofac Surg 2022; 33:1540-1544. [DOI: 10.1097/scs.0000000000008625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 02/19/2022] [Indexed: 11/25/2022] Open
|
13
|
Artificial Intelligence for Classifying and Archiving Orthodontic Images. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1473977. [PMID: 35127938 PMCID: PMC8813223 DOI: 10.1155/2022/1473977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/17/2021] [Accepted: 01/04/2022] [Indexed: 01/03/2023]
Abstract
One of the main requirements for orthodontic treatment is continuous image acquisition. However, the conventional system of orthodontic image acquisition, which includes manual classification, archiving, and monitoring, is time-consuming and prone to errors caused by fatigue. This study is aimed at developing an effective artificial intelligence tool for the automated classification and monitoring of orthodontic images. We comprehensively evaluated the ability of a deep learning model based on Deep hidden IDentity (DeepID) features to classify and archive photographs and radiographs. This evaluation was performed using a dataset of >14,000 images encompassing all 14 categories of orthodontic images. Our model automatically classified orthodontic images in an external dataset with an accuracy of 0.994 and macro area under the curve of 1.00 in 0.08 min. This was 236 times faster than a human expert (18.93 min). Furthermore, human experts with deep learning assistance required an average of 8.10 min to classify images in the external dataset, much shorter than 18.93 min. We conclude that deep learning can improve the accuracy, speed, and efficiency of classification, archiving, and monitoring of orthodontic images.
Collapse
|
14
|
|
15
|
Tagde P, Tagde S, Bhattacharya T, Tagde P, Chopra H, Akter R, Kaushik D, Rahman MH. Blockchain and artificial intelligence technology in e-Health. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:52810-52831. [PMID: 34476701 PMCID: PMC8412875 DOI: 10.1007/s11356-021-16223-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/24/2021] [Indexed: 05/21/2023]
Abstract
Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.
Collapse
Affiliation(s)
- Priti Tagde
- Bhabha Pharmacy Research Institute, Bhabha University Bhopal, Bhopal M.P, India.
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India.
| | - Sandeep Tagde
- PRISAL Foundation (Pharmaceutical Royal International Society), New delhi, India
| | - Tanima Bhattacharya
- School of Chemistry & Chemical Engineering, Hubei University, Wuhan, China
- Department of Science & Engineering, Novel Global Community Education Foundation, Hebersham, Australia
| | - Pooja Tagde
- Practice of Medicine Department, Govt. Homeopathy College, Bhopal, M.P, India
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Rajpura, Punjab, 140401, India
| | - Rokeya Akter
- Department of Pharmacy, Jagannath University, Sadarghat, Dhaka, 1100, Bangladesh
| | - Deepak Kaushik
- Department of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Md Habibur Rahman
- Department of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh.
| |
Collapse
|
16
|
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: 60] [Impact Index Per Article: 20.0] [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.
Collapse
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
| |
Collapse
|
17
|
Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9751564. [PMID: 34258283 PMCID: PMC8245240 DOI: 10.1155/2021/9751564] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/30/2021] [Accepted: 06/05/2021] [Indexed: 11/18/2022]
Abstract
Objective The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry. Materials and Methods Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted. Results The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics. Conclusion The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.
Collapse
|
18
|
Mohammad-Rahimi H, Nadimi M, Rohban MH, Shamsoddin E, Lee VY, Motamedian SR. Machine learning and orthodontics, current trends and the future opportunities: A scoping review. Am J Orthod Dentofacial Orthop 2021; 160:170-192.e4. [PMID: 34103190 DOI: 10.1016/j.ajodo.2021.02.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/01/2021] [Accepted: 02/01/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology show promising results in the practice of orthodontics. This scoping review aimed to investigate the effectiveness of AI-based models employed in orthodontic landmark detection, diagnosis, and treatment planning. METHODS A precise search of electronic databases was conducted, including PubMed, Google Scholar, Scopus, and Embase (English publications from January 2010 to July 2020). Quality Assessment and Diagnostic Accuracy Tool 2 (QUADAS-2) was used to assess the quality of the articles included in this review. RESULTS After applying inclusion and exclusion criteria, 49 articles were included in the final review. AI technology has achieved state-of-the-art results in various orthodontic applications, including automated landmark detection on lateral cephalograms and photography images, cervical vertebra maturation degree determination, skeletal classification, orthodontic tooth extraction decisions, predicting the need for orthodontic treatment or orthognathic surgery, and facial attractiveness. Most of the AI models used in these applications are based on artificial neural networks. CONCLUSIONS AI can help orthodontists save time and provide accuracy comparable to the trained dentists in diagnostic assessments and prognostic predictions. These systems aim to boost performance and enhance the quality of care in orthodontics. However, based on current studies, the most promising application was cephalometry landmark detection, skeletal classification, and decision making on tooth extractions.
Collapse
Affiliation(s)
| | - Mohadeseh Nadimi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Erfan Shamsoddin
- National Institute for Medical Research Development, Tehran, Iran
| | | | - Saeed Reza Motamedian
- Department of Orthodontics, School of Dentistry, & Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
19
|
Ren R, Luo H, Su C, Yao Y, Liao W. Machine learning in dental, oral and craniofacial imaging: a review of recent progress. PeerJ 2021; 9:e11451. [PMID: 34046262 PMCID: PMC8136280 DOI: 10.7717/peerj.11451] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 04/22/2021] [Indexed: 02/05/2023] Open
Abstract
Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.
Collapse
Affiliation(s)
- Ruiyang Ren
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Haozhe Luo
- School of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Chongying Su
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Yang Yao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Wen Liao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Orthodontics, Osaka Dental University, Hirakata, Osaka, Japan
| |
Collapse
|
20
|
Shan B, Werger M, Huang W, Giddon DB. Quantitating the art and science of esthetic clinical success. J World Fed Orthod 2021; 10:49-58. [PMID: 33933391 DOI: 10.1016/j.ejwf.2021.03.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 03/23/2021] [Accepted: 03/23/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Beginning with the biobehavioral bases of esthetic experiences, this article presents a quantitative analytic review of the motives and methods of providers and consumers of orthodontic treatment. METHOD A primary focus is determining the anthropometric bases of self and others' perceived preference and satisfaction with changes in facial appearance. These quantitative analyses have been based on determining the frequency and magnitude of reliability and validity measures of diagnosis, treatment, and satisfaction outcome. Socioeconomic considerations are also quantitated regarding the discrepancy between objective need for treatment as determined for example by the Index of Orthodontic Treatment Need and the subjective demand for treatment. RESULTS The major contribution of this article is the quantitation of the components of esthetic experience from sensation of perception using psycho physical methods, such as Perceptometrics, for determining the morphological basis of perceived facial attractiveness adjusted for ethnocultural differences updated by 3-dimensional and artificial intelligence technology. Recent quantitation of smile components has also added to the measures of esthetically successful treatment. Further contribution of orthodontists to mental and physical health is demonstrated by the differences between perceived personality attributes in profile and full-frontal views of symmetric and asymmetric faces. Such information can facilitate the clinician's ability to determine the ideational representation of the patients' perceived pre- and post-treatment outcome. CONCLUSION The quantitative analysis of the motives and methods involved in the orthodontic treatment process has been combined with the neurophysiological correlates of producing and observing/evaluation of the esthetic experiences of both patients and orthodontists/dentists.
Collapse
Affiliation(s)
- Bo Shan
- DMD Program, Rutgers School of Dental Medicine, Newark, NJ
| | - Marisa Werger
- DMD Candidate Class of 2022, Harvard School of Dental Medicine, Boston, MA
| | - Wei Huang
- Department of Orthodontics, Rutgers School of Dental Medicine, Newark, NJ
| | - Donald B Giddon
- Developmental Biology, Harvard School of Dental Medicine, Boston, MA.
| |
Collapse
|
21
|
Sin Ç, Akkaya N, Aksoy S, Orhan K, Öz U. A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res 2021; 24 Suppl 2:117-123. [DOI: 10.1111/ocr.12480] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/23/2021] [Accepted: 02/17/2021] [Indexed: 12/22/2022]
Affiliation(s)
- Çağla Sin
- Faculty of Dentistry Department of Orthodontics Near East University Mersin10 Turkey
| | - Nurullah Akkaya
- Department of Computer Engineering Applied Artificial Intelligence Research Centre Near East University Mersin10 Turkey
| | - Seçil Aksoy
- Faculty of Dentistry Department of Dentomaxillofacial Radiology Near East University Mersin10 Turkey
| | - Kaan Orhan
- Faculty of Dentistry Department of Dentomaxillofacial Radiology Ankara University Ankara Turkey
- Medical Design Application and Research Center (MEDITAM) Ankara University Ankara Turkey
| | - Ulaş Öz
- Faculty of Dentistry Department of Orthodontics Near East University Mersin10 Turkey
| |
Collapse
|
22
|
Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, Sarode SC, Bhandi S. Developments, application, and performance of artificial intelligence in dentistry - A systematic review. J Dent Sci 2020; 16:508-522. [PMID: 33384840 PMCID: PMC7770297 DOI: 10.1016/j.jds.2020.06.019] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/19/2020] [Indexed: 01/24/2023] Open
Abstract
Background/purpose Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. Materials and methods The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000–March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. Results AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. Conclusion These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.
Collapse
Affiliation(s)
- Sanjeev B Khanagar
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Ali Al-Ehaideb
- Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.,King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.,Dental Services, King Abdulaziz Medical City- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Prabhadevi C Maganur
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Satish Vishwanathaiah
- Department of Preventive Dental Sciences, Division of Pedodontics, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan, Saudi Arabia
| | - Hosam A Baeshen
- Consultant in Orthodontics, Department of Orthodontics, College of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sachin C Sarode
- Department of Oral and Maxillofacial Pathology, Dr. D.Y.Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411018, Maharashtra, India
| | - Shilpa Bhandi
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Saudi Arabia
| |
Collapse
|
23
|
Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - A systematic review. J Dent Sci 2020; 16:482-492. [PMID: 33384838 PMCID: PMC7770284 DOI: 10.1016/j.jds.2020.05.022] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 05/27/2020] [Indexed: 12/16/2022] Open
Abstract
Background/purpose In the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment planning, and predicting the prognosis. Materials and methods The literature for this paper was identified and selected by performing a thorough search for articles in the electronic data bases like Pubmed, Medline, Embase, Cochrane, and Google scholar, Scopus and Web of science, Saudi digital library published over the past two decades (January 2000–February 2020). After applying the inclusion and exclusion criteria, 16 articles were read in full and critically analyzed. QUADAS-2 were adapted for quality analysis of the studies included. Results AI technology has been widely applied for identifying cephalometric landmarks, determining need for orthodontic extractions, determining the degree of maturation of the cervical vertebra, predicting the facial attractiveness after orthognathic surgery, predicting the need for orthodontic treatment, and orthodontic treatment planning. Most of these artificial intelligence models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). Conclusion The results from these reported studies are suggesting that these automated systems have performed exceptionally well, with an accuracy and precision similar to the trained examiners. These systems can simplify the tasks and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently. These systems can be of great value in orthodontics.
Collapse
|
24
|
Dentronics: Towards robotics and artificial intelligence in dentistry. Dent Mater 2020; 36:765-778. [PMID: 32349877 DOI: 10.1016/j.dental.2020.03.021] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 11/21/2022]
Abstract
OBJECTIVES This paper provides an overview of existing applications and concepts of robotic systems and artificial intelligence in dentistry. This review aims to provide the community with novel inputs and argues for an increased utilization of these recent technological developments, referred to as Dentronics, in order to advance dentistry. METHODS First, background on developments in robotics, artificial intelligence (AI) and machine learning (ML) are reviewed that may enable novel assistive applications in dentistry (Sec A). Second, a systematic technology review that evaluates existing state-of-the-art applications in AI, ML and robotics in the context of dentistry is presented (Sec B). RESULTS A systematic literature research in pubmed yielded in a total of 558 results. 41 studies related to ML, 53 studies related to AI and 49 original research papers on robotics application in dentistry were included. ML and AI have been applied in dental research to analyze large amounts of data to eventually support dental decision making, diagnosis, prognosis and treatment planning with the help of data-driven analysis algorithms based on machine learning. So far, only few robotic applications have made it to reality, mostly restricted to pilot use cases. SIGNIFICANCE The authors believe that dentistry can greatly benefit from the current rise of digital human-centered automation and be transformed towards a new robotic, ML and AI-enabled era. In the future, Dentronics will enhance reliability, reproducibility, accuracy and efficiency in dentistry through the democratized use of modern dental technologies, such as medical robot systems and specialized artificial intelligence. Dentronics will increase our understanding of disease pathogenesis, improve risk-assessment-strategies, diagnosis, disease prediction and finally lead to better treatment outcomes.
Collapse
|