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Nordblom NF, Büttner M, Schwendicke F. Artificial Intelligence in Orthodontics: Critical Review. J Dent Res 2024:220345241235606. [PMID: 38682436 DOI: 10.1177/00220345241235606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024] Open
Abstract
With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.
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Affiliation(s)
- N F Nordblom
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - M Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - F Schwendicke
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, Munich, Germany
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Büttner M, Leser U, Schneider L, Schwendicke F. Natural Language Processing: Chances and Challenges in Dentistry. J Dent 2024; 141:104796. [PMID: 38072335 DOI: 10.1016/j.jdent.2023.104796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023] Open
Abstract
INTRODUCTION Natural language processing (NLP) is an intersection between Computer Science and Linguistic which aims to enable machines to process and understand human language. We here summarized applications and limitations of NLP in dentistry. DATA AND SOURCES Narrative review. FINDINGS NLP has evolved increasingly fast. For the dental domain, relevant NLP applications are text classification (e.g., symptom classification) and natural language generation and understanding (e.g., clinical chatbots assisting professionals in office work and patient communication). Analyzing large quantities of text will allow understanding diseases and their trajectories and support a more precise and personalized care. Speech recognition systems may serve as virtual assistants and facilitate automated documentation. However, to date, NLP has rarely been applied in dentistry. Existing research focuses mainly on rule-based solutions for narrow tasks. Technologies such as Recurrent Neural Networks and Transformers have been shown to surpass the language processing capabilities of such rule-based solutions in many fields, but are data-hungry (i.e., rely on large amounts of training data), which limits their application in the dental domain at present. Technologies such as federated or transfer learning or data sharing concepts may allow to overcome this limitation, while challenges in terms of explainability, reproducibility, generalizability and evaluation of NLP in dentistry remain to be resolved for enabling approval of such technologies in medical devices and services. CONCLUSIONS NLP will become a cornerstone of a number of applications in dentistry. The community is called to action to improve the current limitations and foster reliable, high-quality dental NLP. CLINICAL SIGNIFICANCE NLP for text classification (e.g., dental symptom classification) and language generation and understanding (e.g., clinical chatbots, speech recognition) will support administrative tasks in dentistry, provide deeper insights for clinicians and support research and education.
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Affiliation(s)
- Martha Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany.
| | - Ulf Leser
- Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lisa Schneider
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany
| | - Falk Schwendicke
- Clinic for Operative, Preventive and Pediatric Dentistry and Periodontology, Ludwig-Maximilians-University, Munich, Germany
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Ying S, Huang F, Liu W, He F. Deep learning in the overall process of implant prosthodontics: A state-of-the-art review. Clin Implant Dent Relat Res 2024. [PMID: 38286659 DOI: 10.1111/cid.13307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/31/2024]
Abstract
Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.
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Affiliation(s)
- Shunv Ying
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Feng Huang
- School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Wei Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Fuming He
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
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Rahman MA, Victoros E, Ernest J, Davis R, Shanjana Y, Islam MR. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. Clin Pathol 2024; 17:2632010X241226887. [PMID: 38264676 PMCID: PMC10804900 DOI: 10.1177/2632010x241226887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 01/25/2024]
Abstract
The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.
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Affiliation(s)
| | | | - Julianne Ernest
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Rob Davis
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Yeasna Shanjana
- Department of Environmental Sciences, North South University, Bashundhara, Dhaka, Bangladesh
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Dot G, Gajny L, Ducret M. [The challenges of artificial intelligence in odontology]. Med Sci (Paris) 2024; 40:79-84. [PMID: 38299907 DOI: 10.1051/medsci/2023199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024] Open
Abstract
Artificial intelligence has numerous potential applications in dentistry, as these algorithms aim to improve the efficiency and safety of several clinical situations. While the first commercial solutions are being proposed, most of these algorithms have not been sufficiently validated for clinical use. This article describes the challenges surrounding the development of these new tools, to help clinicians to keep a critical eye on this technology.
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Affiliation(s)
- Gauthier Dot
- UFR odontologie, université Paris Cité, Paris, France - AP-HP, hôpital Pitié-Salpêtrière, service de médecine bucco-dentaire, Paris, France - Institut de biomécanique humaine Georges Charpak, école nationale supérieure d'Arts et Métiers, Paris, France
| | - Laurent Gajny
- Institut de biomécanique humaine Georges Charpak, école nationale supérieure d'Arts et Métiers, Paris, France
| | - Maxime Ducret
- Faculté d'odontologie, université Claude Bernard Lyon 1, hospices civils de Lyon, Lyon, France
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Singh N, Pandey A, Tikku AP, Verma P, Singh BP. Attitude, perception and barriers of dental professionals towards artificial intelligence. J Oral Biol Craniofac Res 2023; 13:584-588. [PMID: 37576799 PMCID: PMC10415790 DOI: 10.1016/j.jobcr.2023.06.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 06/27/2023] [Indexed: 08/15/2023] Open
Abstract
Aim To know attitudes, perceptions and barriers towards the use of Artificial Intelligence (AI) in dentistry in India among undergraduate and postgraduate students. Methodology A questionnaire-based cross-sectional study was conducted among participants pursuing graduation and postgraduation. The questionnaire consisted of 23 close-ended and 2 open-ended questions divided into various sections of attitude, perception and barriers. The data was analysed using Statistical Package for Social Sciences (SPSS) version 24.0. Result Out of 937 responses, 55.2% responded that they get information about AI from social media platforms. 51.3% of respondents have basic knowledge about the use of AI in dentistry. 59.6% agreed that AI can be used as a "definitive diagnostic tool" in the diagnosis of diseases. 66.5% agreed that AI can be used for radiographic diagnosis of tooth caries. 71.3% stated that AI can be used as a "treatment planning tool" in dentistry. 55.7% stated that AI should be part of undergraduate dental training. Conclusion This study concluded that both dental students are aware of the concept of AI. Participants were positive when asked if AI can increase the efficiency of diagnosis, prognosis and treatment planning procedures as well as in managing patient data. Both participants believed that the barriers to the introduction of AI in dentistry are a lack of technical resources and a lack of training personnel in college.
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Affiliation(s)
- Nishi Singh
- Department of Conservative Dentistry & Endodontics, Faculty of Dentistry, King George's Medical University (KGMU), Lucknow, UP, India
| | - Anushka Pandey
- Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Aseem Prakash Tikku
- Department of Conservative Dentistry & Endodontics, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Promila Verma
- Department of Conservative Dentistry & Endodontics, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Balendra Pratap Singh
- Department of Prosthodontics and Crown & Bridge, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
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Schneider L, Rischke R, Krois J, Krasowski A, Büttner M, Mohammad-Rahimi H, Chaurasia A, Pereira NS, Lee JH, Uribe SE, Shahab S, Koca-Ünsal RB, Ünsal G, Martinez-Beneyto Y, Brinz J, Tryfonos O, Schwendicke F. Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs. J Dent 2023; 135:104556. [PMID: 37209769 DOI: 10.1016/j.jdent.2023.104556] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023] Open
Abstract
OBJECTIVE Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs. METHODS We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n = 143 to n = 1881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers. RESULTS For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability. CONCLUSION If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high. CLINICAL SIGNIFICANCE This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.
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Affiliation(s)
- Lisa Schneider
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Roman Rischke
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Joachim Krois
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Aleksander Krasowski
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Martha Büttner
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Hossein Mohammad-Rahimi
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Shahid Beheshti University of Medical Sciences, Tehran, Iran Dental school, Iran
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George's Medical University, Lucknow, India
| | - Nielsen S Pereira
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Private Practice in Oral and Maxillofacial Radiology, Rio de Janeiro, Brazil
| | - Jae-Hong Lee
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea
| | - Sergio E Uribe
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry Oral Health, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia
| | - Shahriar Shahab
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahed University of Medical Sciences, Tehran, Iran
| | - Revan Birke Koca-Ünsal
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology, Faculty of Dentistry, University of Kyrenia, Kyrenia, Cyprus
| | - Gürkan Ünsal
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | | | - Janet Brinz
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany
| | - Olga Tryfonos
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam, Amsterdam, the Netherlands
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland.
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Ding X, Huang Y, Tian X, Zhao Y, Feng G, Gao Z. Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence. Diagnostics (Basel) 2023; 13:2309. [PMID: 37443702 DOI: 10.3390/diagnostics13132309] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/04/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
A common infectious disease, otitis media (OM) has a low rate of early diagnosis, which significantly increases the difficulty of treating the disease and the likelihood of serious complications developing including hearing loss, speech impairment, and even intracranial infection. Several areas of healthcare have shown great promise in the application of artificial intelligence (AI) systems, such as the accurate detection of diseases, the automated interpretation of images, and the prediction of patient outcomes. Several articles have reported some machine learning (ML) algorithms such as ResNet, InceptionV3 and Unet, were applied to the diagnosis of OM successfully. The use of these techniques in the OM is still in its infancy, but their potential is enormous. We present in this review important concepts related to ML and AI, describe how these technologies are currently being applied to diagnosing, treating, and managing OM, and discuss the challenges associated with developing AI-assisted OM technologies in the future.
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Affiliation(s)
- Xin Ding
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Yu Huang
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Xu Tian
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Yang Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Guodong Feng
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
| | - Zhiqiang Gao
- Department of Otorhinolaryngology Head and Neck Surgery, The Peaking Union Medical College Hospital, No. 1, Shuaifuyuan, Dongcheng District, Beijing 100010, China
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Park WS, Huh JK, Lee JH. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset. Sci Rep 2023; 13:4862. [PMID: 36964171 PMCID: PMC10039053 DOI: 10.1038/s41598-023-32118-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/22/2023] [Indexed: 03/26/2023] Open
Abstract
This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.
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Affiliation(s)
- Won-Se Park
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea
- Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul, Korea
| | - Jong-Ki Huh
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea.
- Department of Oral and Maxillofacial Surgery, Gangnam Severance Hospital, Yonsei University College of Dentistry, 211 Eonju-ro, Gangnam-gu, Seoul, 06273, Korea.
| | - Jae-Hong Lee
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea.
- Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, 54896, Korea.
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Troiano G, Nibali L, Petsos H, Eickholz P, Saleh MHA, Santamaria P, Jian J, Shi S, Meng H, Zhurakivska K, Wang HL, Ravidà A. Development and international validation of logistic regression and machine-learning models for the prediction of 10-year molar loss. J Clin Periodontol 2023; 50:348-357. [PMID: 36305042 DOI: 10.1111/jcpe.13739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 12/13/2022]
Abstract
AIM To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. MATERIALS AND METHODS Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine-learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K-nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. RESULTS The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis-related molar loss. In addition, the ensembled model showed the best calibration performance. CONCLUSIONS Through a multi-centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.
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Affiliation(s)
- Giuseppe Troiano
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Luigi Nibali
- Periodontology Unit, Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Hari Petsos
- Department of Periodontology, Center for Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, Frankfurt am Main, Germany
| | - Peter Eickholz
- Department of Periodontology, Center for Dentistry and Oral Medicine (Carolinum), Johann Wolfgang Goethe-University Frankfurt/Main, Frankfurt am Main, Germany
| | - Muhammad H A Saleh
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA
| | - Pasquale Santamaria
- Periodontology Unit, Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Jao Jian
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Shuwen Shi
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Huanxin Meng
- Department of Periodontology, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, China
| | - Khrystyna Zhurakivska
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Hom-Lay Wang
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA
| | - Andrea Ravidà
- Department of Periodontics and Oral Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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13
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Arsiwala-Scheppach LT, Chaurasia A, Müller A, Krois J, Schwendicke F. Machine Learning in Dentistry: A Scoping Review. J Clin Med 2023; 12. [PMID: 36769585 DOI: 10.3390/jcm12030937] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [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.
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14
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Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol 2023; 52:20220335. [PMID: 36472627 PMCID: PMC9793453 DOI: 10.1259/dmfr.20220335] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.
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Affiliation(s)
- Kuo Feng Hung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Michael M. Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
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15
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:diagnostics13010110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Correspondence:
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16
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Affiliation(s)
- F Schwendicke
- Department of Oral Diagnostics, Digital Health, Health Services Research, Charité - Universitätsmedizin, Berlin, Germany
| | - M L Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, and Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
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