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Beak W, Park J, Ji S. Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation. Heliyon 2024; 10:e32496. [PMID: 38912435 PMCID: PMC11193031 DOI: 10.1016/j.heliyon.2024.e32496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 06/25/2024] Open
Abstract
Objectives This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease. Methods The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics. Results Correlation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average. Conclusions Data-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal-external validations.
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Affiliation(s)
- Woosun Beak
- Department of Dental Public Health, Ajou University Graduate School of Clinical Dentistry, Suwon, Republic of Korea
- Department of Dentistry, Gyeonggi Provincial Medical Center Suwon Hospital, Suwon, Republic of Korea
| | - Jihun Park
- Department of Materials Science and Engineering, University of Maryland, College Park, MD, USA
| | - Suk Ji
- Department of Dental Public Health, Ajou University Graduate School of Clinical Dentistry, Suwon, Republic of Korea
- Department of Periodontology, Institute of Oral Health Science, Ajou University School of Medicine, Suwon, Republic of Korea
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Blum IR. Primary care dentistry: Past, present and future. J Dent 2024; 145:105007. [PMID: 38677403 DOI: 10.1016/j.jdent.2024.105007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/29/2024] Open
Abstract
This article examines the past, present and future of primary care dentistry. It provides a historical background of primary care dentistry and describes stages of its evolution. It further reviews the purpose and mission of contemporary primary care dentistry and outlines a vision for the development of primary care dentistry in the future. The type and extent of innovations and technological advances that have impacted - and improved - primary care dentistry revolutionising clinical activities, ranging from early computerised tomography to modern digital systems and workflows are summarised. A discussion of current scientific evidence base pertinent to primary care dentistry highlighting the need for 'effectiveness' rather than 'efficacy' studies is included in order to provide research data pertinent to the primary care dentistry setting where most dental patients receive most of their care most of the time.
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Affiliation(s)
- Igor R Blum
- Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK; King's College Hospital Dental Institute, London, UK.
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3
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Salazar D, Rossouw PE, Javed F, Michelogiannakis D. Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review. J Orthod 2024; 51:107-119. [PMID: 37772513 DOI: 10.1177/14653125231203743] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
BACKGROUND The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed. OBJECTIVES To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT. DESIGN Systematic review. DATA SOURCES Unrestricted search of indexed databases and reference lists of included studies. DATA SELECTION Clinical studies that addressed the focused question 'Is AI useful for treatment planning and soft tissue outcome prediction in OGT?' were included. DATA EXTRACTION Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration's RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively. DATA SYNTHESIS Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB. LIMITATIONS Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment. CONCLUSION AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT. REGISTRATION PROSPERO CRD42022366864.
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Affiliation(s)
- Daisy Salazar
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Paul Emile Rossouw
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Fawad Javed
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
| | - Dimitrios Michelogiannakis
- Department of Orthodontics and Dentofacial Orthopedics, Eastman Institute for Oral Health, University of Rochester, Rochester, NY, USA
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Zayed SO, Abd-Rabou RYM, Abdelhameed GM, Abdelhamid Y, Khairy K, Abulnoor BA, Ibrahim SH, Khaled H. The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study. BMC Oral Health 2024; 24:598. [PMID: 38778322 PMCID: PMC11112957 DOI: 10.1186/s12903-024-04347-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases. OBJECTIVE The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs? METHOD The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity). RESULTS The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree). CONCLUSION The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.
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Affiliation(s)
- Shaimaa O Zayed
- Department of Oral maxillofacial Pathology, Faculty of Dentistry, Cairo University, Cairo, Egypt
- Department of Oral Pathology, Misr University for Science and Technology, P. O. Box 77, Giza, Egypt
| | - Rawan Y M Abd-Rabou
- Faculty of Oral Medicine & Dental Surgery, Misr University for Science and Technology, P. O. Box 77, Giza, Egypt
| | | | - Youssef Abdelhamid
- Philosophy & Interactive Media Minors, New York University, Abu Dhabi, United Arab Emirates
| | | | - Bassam A Abulnoor
- Fixes Prosthodontics, Faculty of Dentistry, Ain Shams University, Cairo, Egypt
| | | | - Heba Khaled
- Lecturer of Oral Maxillofacial Pathology, Faculty of Dentistry, Cairo University, Cairo, Egypt
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Buduru S, Cofar F, Mesaroș A, Tăut M, Negucioiu M, Almășan O. Perceptions in Digital Smile Design: Assessing Laypeople and Dental Professionals' Preferences Using an Artificial-Intelligence-Based Application. Dent J (Basel) 2024; 12:104. [PMID: 38668016 PMCID: PMC11049051 DOI: 10.3390/dj12040104] [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: 02/15/2024] [Revised: 04/01/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Digital Smile Design (DSD) is used in many fields of dentistry. This prospective observational study assessed laypeople's and dental professionals' perceptions of a DSD application. SmileCloud, an online DSD platform, was used to create two different designs for three patients; after that, the participants, in a 30-question online illustrated survey, were asked about the most attractive design and other features of the smile. Dentists' and laypeople's perceptions about specific DSD features were assessed. The Kolmogorov-Smirnov normality test was used. Descriptive and crosstab analyses compared the respondents' opinions for each statement. Chi-square tests were used to determine the relationship between the questions and any association with age, gender, and profession. The test results were rated as significant at a p-value < 0.05. A total of 520 participants (dental professionals, students, dental technicians, and laypeople) were enrolled. The statistically significant features were self-esteem related to appearance (p = 0.05), facial and smile symmetry (p = 0.42, p < 0.0001), tooth color (p = 0.012), and symmetry of gums (p < 0.001). For each patient, the design with dominant round upper incisors and perfect symmetry was preferred (p < 0.001). Digital pre-visualization benefits diagnosis and enriches treatment planning. The dentist-dental technician-patient team should be involved in the decision-making process of pre-visualization.
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Affiliation(s)
- Smaranda Buduru
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hațieganu University of Medicine and Pharmacy, 32 Clinicilor Street, 400006 Cluj-Napoca, Romania; (S.B.); (A.M.); (O.A.)
| | - Florin Cofar
- Doctoral School, Dental Medicine, Victor Babeş University of Medicine and Pharmacy, 300041 Timișoara, Romania;
| | - Anca Mesaroș
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hațieganu University of Medicine and Pharmacy, 32 Clinicilor Street, 400006 Cluj-Napoca, Romania; (S.B.); (A.M.); (O.A.)
| | - Manuela Tăut
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hațieganu University of Medicine and Pharmacy, 32 Clinicilor Street, 400006 Cluj-Napoca, Romania; (S.B.); (A.M.); (O.A.)
| | - Marius Negucioiu
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hațieganu University of Medicine and Pharmacy, 32 Clinicilor Street, 400006 Cluj-Napoca, Romania; (S.B.); (A.M.); (O.A.)
| | - Oana Almășan
- Prosthetic Dentistry and Dental Materials Department, Iuliu Hațieganu University of Medicine and Pharmacy, 32 Clinicilor Street, 400006 Cluj-Napoca, Romania; (S.B.); (A.M.); (O.A.)
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Lin GSS, Tan WW, Hashim H. Students' perceptions towards the ethical considerations of using artificial intelligence algorithms in clinical decision-making. Br Dent J 2024:10.1038/s41415-024-7184-3. [PMID: 38491204 DOI: 10.1038/s41415-024-7184-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/01/2023] [Indexed: 03/18/2024]
Abstract
Aim The present study aimed to explore the perceptions of dental students regarding the ethical considerations associated with the use of artificial intelligence (AI) algorithms in clinical decision-making.Methods All the undergraduate clinical-year dental students were invited to take part in the study. A validated online questionnaire which consisted of 21 closed-ended questions (five-point Likert scales) was distributed to the students to evaluate their perceptions on the topic. Mean perception scores of the students from different years were analysed using a one-way ANOVA test, while independent t-tests were used to compare the scores between sexes.Results In total, 165 students participated in the present study. The mean age of the respondents was 23.3 (± 1.38) years and the majority were female, Chinese students. Respondents showed positive perceptions throughout all three domains. Uniform and comparable perceptions were seen across various academic years and sexes, with female respondents expressing stronger agreement regarding patient consent and privacy prioritisation.Conclusion Undergraduate clinical dental students generally showed positive perceptions regarding the ethical considerations associated with the integration of AI algorithms in clinical decision-making. It is essential to address these ethical considerations to ensure that AI benefits patient outcomes while upholding fundamental ethical principles and patient-centred care.
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Affiliation(s)
- Galvin Sim Siang Lin
- Department of Restorative Dentistry, Kulliyyah of Dentistry, International Islamic University Malaysia, 25200, Pahang, Malaysia.
| | - Wen Wu Tan
- Department of Dental Public Health, Faculty of Dentistry, AIMST University, 08100, Kedah, Malaysia
| | - Hasnah Hashim
- Department of Dental Public Health, Faculty of Dentistry, AIMST University, 08100, Kedah, Malaysia
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Umer F, Adnan S, Lal A. Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review. BMC Oral Health 2024; 24:220. [PMID: 38347508 PMCID: PMC10860267 DOI: 10.1186/s12903-024-03970-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/02/2024] [Indexed: 02/15/2024] Open
Abstract
Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.
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Affiliation(s)
- Fahad Umer
- Department of Surgery, Section of Dentistry, The Aga Khan University, Karachi, Pakistan
| | - Samira Adnan
- Department of Operative Dentistry, Sindh Institute of Oral Health Sciences, Jinnah Sindh Medical University, Karachi, Pakistan
| | - Abhishek Lal
- Department of Medicine, Section of Gastroenterology, The Aga Khan University, Karachi, Pakistan.
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8
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Fan FY, Lin WC, Huang HY, Shen YK, Chang YC, Li HY, Ruslin M, Lee SY. Applying machine learning to assess the morphology of sculpted teeth. J Dent Sci 2024; 19:542-549. [PMID: 38303893 PMCID: PMC10829735 DOI: 10.1016/j.jds.2023.09.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/21/2023] [Indexed: 02/03/2024] Open
Abstract
Background/purpose Producing tooth crowns through dental technology is a basic function of dentistry. The morphology of tooth crowns is the most important parameter for evaluating its acceptability. The procedures were divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation. Materials and methods Dental plaster rods were prepared. The effective data collected were to classify 121 teeth (15th tooth position), 342 teeth (16th tooth position), 69 teeth (21st tooth position), and 89 teeth (43rd tooth position), for a total of 621 teeth. The procedures are divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation. Results The area under the curve (AUC) value was 0, 0.5, and 0.72 in this study. The precision rate and recall rate of micro-averaging/macro-averaging were 0.75/0.73 and 0.75/0.72. If we took a newly carved tooth picture into the program, the current effectiveness of machine learning was about 70%-75% to evaluate the quality of tooth morphology. Through the calculation and analysis of the two different concepts of micro-average/macro-average and AUC, similar values could be obtained. Conclusion This study established a set of procedures that can judge the quality of hand-carved plaster sticks and teeth, and the accuracy rate is about 70%-75%. It is expected that this process can be used to assist dental technicians in judging the pros and cons of hand-carved plaster sticks and teeth, so as to help dental technicians to learn the tooth morphology more effectively.
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Affiliation(s)
- Fang-Yu Fan
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wei-Chun Lin
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Dentistry, Wan-Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Center for Tooth Bank and Dental Stem Cell Technology, Taipei Medical University, Taipei, Taiwan
| | - Huei-Yu Huang
- Department of Dentistry, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yung-Kang Shen
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Oral Biology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Yung-Chun Chang
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Heng-Yu Li
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Muhammad Ruslin
- Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Hasanuddin University, Makassar, Indonesia
| | - Sheng-Yang Lee
- Department of Dentistry, Wan-Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Center for Tooth Bank and Dental Stem Cell Technology, Taipei Medical University, Taipei, Taiwan
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
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Farajollahi M, Safarian MS, Hatami M, Esmaeil Nejad A, Peters OA. Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss-A scoping review. AUST ENDOD J 2023; 49:720-734. [PMID: 37439465 DOI: 10.1111/aej.12775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.
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Affiliation(s)
- Mehran Farajollahi
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Sadegh Safarian
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Masoud Hatami
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Esmaeil Nejad
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ove A Peters
- School of Dentistry, The University of Queensland, Herston, Queensland, Australia
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Surlari Z, Budală DG, Lupu CI, Stelea CG, Butnaru OM, Luchian I. Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry-A Narrative Review. J Clin Med 2023; 12:7378. [PMID: 38068430 PMCID: PMC10707023 DOI: 10.3390/jcm12237378] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 07/25/2024] Open
Abstract
The concept of machines learning and acting like humans is what is meant by the phrase "artificial intelligence" (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.
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Affiliation(s)
- Zinovia Surlari
- Department of Fixed Protheses, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Dana Gabriela Budală
- Department of Implantology, Removable Prostheses, Dental Prostheses Technology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Costin Iulian Lupu
- Department of Dental Management, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Carmen Gabriela Stelea
- Department of Oral Surgery, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Oana Maria Butnaru
- Department of Biophysics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 Universității Street, 700115 Iasi, Romania;
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Asgary S. Emphasizing the impact of artificial intelligence in dentistry: A call for integration and exploration. J Dent Sci 2023; 18:1929-1930. [PMID: 37799921 PMCID: PMC10547989 DOI: 10.1016/j.jds.2023.06.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 06/26/2023] [Indexed: 10/07/2023] Open
Affiliation(s)
- Saeed Asgary
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Evin, Tehran, Iran
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12
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Lin GSS, Ng YS, Ghani NRNA, Chua KH. Revolutionising dental technologies: a qualitative study on dental technicians' perceptions of Artificial intelligence integration. BMC Oral Health 2023; 23:690. [PMID: 37749537 PMCID: PMC10521564 DOI: 10.1186/s12903-023-03389-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND The integration of artificial intelligence (AI) in dentistry has the potential to revolutionise the field of dental technologies. However, dental technicians' views on the use of AI in dental technology are still sparse in the literature. This qualitative study aimed to explore the perceptions of dental technicians regarding the use of AI in their dental laboratory practice. METHODS Twelve dental technicians with at least five years of professional experience and currently working in Malaysia agreed to participate in the one-to-one in-depth online interviews. Interviews were recorded, transcribed verbatim and translated. Thematic analysis was conducted to identify patterns, themes, and categories within the interview transcripts. RESULTS The analysis revealed two key themes: "Perceived Benefits of AI" and "Concerns and Challenges". Dental technicians recognised the enhanced efficiency, productivity, accuracy, and precision that AI can bring to dental laboratories. They also acknowledged the streamlined workflow and improved communication facilitated by AI systems. However, concerns were raised regarding job security, professional identity, ethical considerations, and the need for adequate training and support. CONCLUSION This research sheds light on the potential benefits and challenges associated with the integration of AI in dental laboratory practices. Understanding these perceptions and addressing the challenges can support the effective integration of AI in dental laboratories and contribute to the growing body of literature on AI in healthcare.
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Affiliation(s)
- Galvin Sim Siang Lin
- Department of Dental Materials, Faculty of Dentistry, Asian Institute of Medicine, Science and Technology (AIMST) University, 08100, Bedong, Kedah, Malaysia.
| | - Yook Shiang Ng
- Conservative Dentistry Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Nik Rozainah Nik Abdul Ghani
- Conservative Dentistry Unit, School of Dental Sciences, Universiti Sains Malaysia, Health Campus, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Kah Hoay Chua
- Department of Dental Technology, Faculty of Dentistry, Asian Institute of Medicine, Science and Technology (AIMST) University, 08100, Bedong, Kedah, Malaysia
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Murad M, Tamimi F. Artificial intelligence: is it more accurate than endodontists in root canal therapy? Evid Based Dent 2023; 24:106-107. [PMID: 37221364 DOI: 10.1038/s41432-023-00901-8] [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: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 05/25/2023]
Abstract
DATA SOURCES The following databases were electronically searched (up to 20 March 2022): PubMed, Scopus, Google Scholar, and Cochrane Library. This was followed by hand-searching the reference lists of the included articles. The search was restricted to articles published in English. The aim of this study was to evaluate the effectiveness of artificial intelligence in identifying, analyzing, and interpreting radiographic features related to endodontic therapy. STUDY SELECTION The selection criteria were limited to trials evaluating the effectiveness of artificial intelligence in identifying, analyzing, and interpreting radiographic features related to endodontic therapy. TYPES OF STUDIES Clinical, ex-vivo, and in-vitro trials. TYPES OF RADIOGRAPHIC IMAGES Two-dimensional intra-oral imaging (bitewings and/or periapicals), panoramic radiographs (PRs), and cone beam computed tomography (CBCT). EXCLUSION CRITERIA 1) Case reports, letters, and commentaries; 2) Reviews, conferences, and books; 3) Inaccessible reports. DATA EXTRACTION AND SYNTHESIS The titles and abstracts of the results of the searches were screened by two authors against the inclusion criteria. The full text of any potentially relevant abstract and title were retrieved for more comprehensive assessment. The risk of bias was assessed initially by two examiners and then by two authors. Any discrepancies were resolved through discussion and consensus. RESULTS Out of the 1131 articles which were identified in the initial search, 30 were considered relevant, and only 24 articles were eventually included. The exclusion of the six articles was related to the absence of appropriate clinical or radiological data. Meta-analysis was not performed due to high heterogeneity. Various degrees of bias were detected in more than 58% of the included studies. CONCLUSIONS Although most of the included studies were biased, the authors concluded that the use of artificial intelligence can be an effective alternative in identifying, analyzing and interpreting radiographic features related to root canal therapy.
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Affiliation(s)
- Mohammed Murad
- Clinical MSc in Endodontics, University of Manchester, Manchester, UK.
- Clinical MSc in Prosthetic Dentistry, University of Bristol, Bristol, UK.
- Division of Clinical Dentistry, The Primary Health Care Corporation, P.O. Box: 26555, Doha, Qatar.
| | - Faleh Tamimi
- College of Dental Medicine, QU Health, Qatar University, Doha, Qatar
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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: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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Ayad N, Schwendicke F, Krois J, van den Bosch S, Bergé S, Bohner L, Hanisch M, Vinayahalingam S. Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey. Head Face Med 2023; 19:23. [PMID: 37349791 PMCID: PMC10288769 DOI: 10.1186/s13005-023-00368-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher's exact tests with Monte Carlo approximation. Patients' perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor-patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1-5 (42.3%) or 5-10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18-35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients' perceptions may allow professionals to shape AI-driven dentistry in the future.
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Affiliation(s)
- Nasim Ayad
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Stefanie van den Bosch
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Stefaan Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Lauren Bohner
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Marcel Hanisch
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
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Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations. Pediatr Res 2023; 93:390-395. [PMID: 36302858 DOI: 10.1038/s41390-022-02356-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022]
Abstract
Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.
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Lyakhov PA, Dolgalev AA, Lyakhova UA, Muraev AA, Zolotayev KE, Semerikov DY. Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants. Front Neuroinform 2022; 16:1067040. [PMID: 36567879 PMCID: PMC9768332 DOI: 10.3389/fninf.2022.1067040] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient's condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making.
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Edvardsen IP, Teterina A, Johansen T, Myhre JN, Godtliebsen F, Bolstad NL. Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016. J Int Med Res 2022; 50:3000605221135147. [DOI: 10.1177/03000605221135147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objective To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width. Methods Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental panoramic radiographs. Four pretrained object detectors were tested. We randomly chose 80% of the data for training and 20% for testing. Models were trained using GeForce RTX 2080 Ti with 11 GB GPU memory (NVIDIA Corporation, Santa Clara, CA, USA). Python programming language version 3.7 was used for analysis. Results The EfficientDet-D0 model showed the highest average precision of 0.30. When the threshold to regard a prediction as correct (intersection over union) was set to 0.5, the average precision was 0.79. The RetinaNet model achieved the lowest average precision of 0.23, and the precision was 0.64 when the intersection over union was set to 0.5. The procedure to estimate mandibular cortical width showed acceptable results. Of 100 random images, the algorithm produced an output 93 times, 20 of which were not visually satisfactory. Conclusions EfficientDet-D0 effectively detected the mental foramen. Methods for estimating bone quality are important in radiology and require further development.
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Affiliation(s)
- Isak Paasche Edvardsen
- Department of Physics and Technology, Faculty of Science and Technology, UiT The Arctic University of Norway, PO Box 6050, Langnes, N-9037 Tromsø, Norway
| | - Anna Teterina
- Department of Clinical Dentistry, Faculty of Health Sciences, UiT The Arctic University of Norway, PO Box 6050, Langnes, N-9037 Tromsø, Norway
| | - Thomas Johansen
- Department of Energy and Technology, NORCE Norwegian Research Center, Sykehusveien 23, 9294 Tromsø, Norway
| | - Jonas Nordhaug Myhre
- Department of Energy and Technology, NORCE Norwegian Research Center, Sykehusveien 23, 9294 Tromsø, Norway
| | - Fred Godtliebsen
- Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway, PO Box 6050, Langnes, N-9037 Tromsø, Norway
| | - Napat Limchaichana Bolstad
- Department of Clinical Dentistry, Faculty of Health Sciences, UiT The Arctic University of Norway, PO Box 6050, Langnes, N-9037 Tromsø, Norway
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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: 33] [Impact Index Per Article: 16.5] [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.
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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.
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Lee SJ, Chung D, Asano A, Sasaki D, Maeno M, Ishida Y, Kobayashi T, Kuwajima Y, Da Silva JD, Nagai S. Diagnosis of Tooth Prognosis Using Artificial Intelligence. Diagnostics (Basel) 2022; 12:diagnostics12061422. [PMID: 35741232 PMCID: PMC9221626 DOI: 10.3390/diagnostics12061422] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/06/2022] [Accepted: 06/07/2022] [Indexed: 02/06/2023] Open
Abstract
The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.
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Affiliation(s)
- Sang J. Lee
- Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, Boston, MA 02115, USA; (S.J.L.); (J.D.D.S.)
| | - Dahee Chung
- Harvard School of Dental Medicine, Boston, MA 02115, USA;
| | - Akiko Asano
- Department of Restorative Dentistry, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, Japan;
| | - Daisuke Sasaki
- Department of Periodontology, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, Japan;
| | - Masahiko Maeno
- Department of Adhesive Dentistry, School of Life Dentistry at Tokyo, The Nippon Dental University, Chiyoda-ku, Tokyo 102-8159, Japan;
| | - Yoshiki Ishida
- Department of Dental Materials Science, School of Life Dentistry at Tokyo, The Nippon Dental University, Chiyoda-ku, Tokyo 102-8159, Japan;
| | - Takuya Kobayashi
- Department of Oral Rehabilitation, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, Japan;
| | - Yukinori Kuwajima
- Department of Orthodontics, School of Dental Medicine, Iwate Medical University, Morioka 020-8505, Japan;
| | - John D. Da Silva
- Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, Boston, MA 02115, USA; (S.J.L.); (J.D.D.S.)
| | - Shigemi Nagai
- Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, MA 02115, USA
- Correspondence: ; Tel.: +1-781-698-9688
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Patil S, Albogami S, Hosmani J, Mujoo S, Kamil MA, Mansour MA, Abdul HN, Bhandi S, Ahmed SSSJ. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics (Basel) 2022; 12:diagnostics12051029. [PMID: 35626185 PMCID: PMC9139975 DOI: 10.3390/diagnostics12051029] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/12/2022] [Accepted: 04/18/2022] [Indexed: 12/19/2022] Open
Abstract
Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.
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Affiliation(s)
- Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence:
| | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia;
| | - Jagadish Hosmani
- Department of Diagnostic Dental Sciences, Oral Pathology Division, Faculty of Dentistry, College of Dentistry, King Khalid University, Abha 61411, Saudi Arabia;
| | - Sheetal Mujoo
- Division of Oral Medicine & Radiology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Mona Awad Kamil
- Department of Preventive Dental Science, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Manawar Ahmad Mansour
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Hina Naim Abdul
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Shilpa Bhandi
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Shiek S. S. J. Ahmed
- Multi-Omics and Drug Discovery Lab, Chettinad Academy of Research and Education, Chennai 600130, India;
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Artificial Intelligence: A New Diagnostic Software in Dentistry: A Preliminary Performance Diagnostic Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031728. [PMID: 35162751 PMCID: PMC8835112 DOI: 10.3390/ijerph19031728] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 12/18/2021] [Accepted: 01/27/2022] [Indexed: 02/01/2023]
Abstract
Background: Artificial intelligence (AI) has taken hold in public health because more and more people are looking to make a diagnosis using technology that allows them to work faster and more accurately, reducing costs and the number of medical errors. Methods: In the present study, 120 panoramic X-rays (OPGs) were randomly selected from the Department of Oral and Maxillofacial Sciences of Sapienza University of Rome, Italy. The OPGs were acquired and analyzed using Apox, which takes a panoramic X-rayand automatically returns the dental formula, the presence of dental implants, prosthetic crowns, fillings and root remnants. A descriptive analysis was performed presenting the categorical variables as absolute and relative frequencies. Results: In total, the number of true positive (TP) values was 2.195 (19.06%); true negative (TN), 8.908 (77.34%); false positive (FP), 132 (1.15%); and false negative (FN), 283 (2.46%). The overall sensitivity was 0.89, while the overall specificity was 0.98. Conclusions: The present study shows the latest achievements in dentistry, analyzing the application and credibility of a new diagnostic method to improve the work of dentists and the patients’ care.
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Nath S, Raveendran R, Perumbure S. Artificial Intelligence and Its Application in the Early Detection of Oral Cancers. CLINICAL CANCER INVESTIGATION JOURNAL 2022. [DOI: 10.51847/h7wa0uhoif] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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25
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Umer F, Habib S. Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review. J Endod 2021; 48:152-160. [PMID: 34838523 DOI: 10.1016/j.joen.2021.11.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) comprises computational models that mimic the human brain to perform various diagnostic tasks in clinical practice. The aim of this scoping review was to systematically analyze the AI algorithms and models used in endodontics and identify the source quality and type of evidence. METHODS A literature search was conducted in October 2020 to identify the relevant literature in English language in the 4 major health sciences databases, ie, MEDLINE, Dentistry & Oral Science, CINAHL Plus, and Cochrane Library. Our review questions were the following: what are the different AI algorithms and models used in endodontics?, what are the datasets being used?, what type of performance metrics were reported?, and what diagnostic performance measures were used?. The quality of the included studies was evaluated by a modified Quality Assessment of Studies of Diagnostic Accuracy risk (QUADAS) tool. RESULTS Out of 300 studies, 12 articles met our inclusion criteria and were subjected to final analysis. Among the included studies, 6 studies focused on periapical pathology, and 3 studies investigated vertical root fractures. Most studies (n = 10) used neural networks, among which convolutional neural networks were commonly used. The datasets that were mostly studied were radiographs. Out of 12 studies, only 3 studies achieved a high score according to the modified QUADAS tool. CONCLUSIONS AI models had acceptable performance, ie, accuracy >90% in executing various diagnostic tasks. The scientific reporting of AI-related research is irregular. The endodontic community needs to implement recommended guidelines to improve the weaknesses in the current planning and reporting of AI-related research to improve its scientific vigor.
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Affiliation(s)
- Fahad Umer
- Department of Surgery, Aga Khan University Hospital, Karachi, Pakistan.
| | - Saqib Habib
- Operative Dentistry and Endodontics, Department of Surgery, Aga Khan University Hospital, Karachi, Pakistan
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Bartold PM. Aged care and artificial intelligence. Aust Dent J 2021; 66:223. [PMID: 34347295 DOI: 10.1111/adj.12867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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