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Griffith A, Chande C, Kulkarni S, Morel J, Cheng YH, Shimizu E, Cugini C, Basuray S, Kumar V. Point-of-care diagnostic devices for periodontitis - current trends and urgent need. SENSORS & DIAGNOSTICS 2024; 3:1119-1134. [PMID: 39007012 PMCID: PMC11238172 DOI: 10.1039/d3sd00317e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/16/2024] [Indexed: 07/16/2024]
Abstract
Point of care (POC) diagnostic devices provide a method for rapid accurate identification of disease through analysis of biologically relevant substances. This review focuses on the utility of POC testing for early detection of periodontitis, a critical factor in treating the disease. Accessing the oral cavity for biological sampling is less invasive when compared to other internal test sites, and oral fluids contain biomarkers indicative of periodontitis. The ease of access makes the mouth an excellent target location for the development of POC devices. In this review, accepted standards in industry by which these devices must adhere, provided by the World Health Organization such as REASSURED and CLIA, are discussed. An overview is provided for many periodontal biomarkers currently being investigated as a means of predicting periodontal disease and its progression. POC devices currently being investigated for the identification and monitoring of periodontal disease such as paper-based and lab-on-a-chip based devices are outlined. Limitations of current POC devices on the market are provided and future directions in leveraging biomarkers as an adjunctive method for oral diagnosis along with AI-based analysis systems are discussed. Here, we present the ESSENCE sensor platform, which combines a porous non-planar electrode with enhanced shear flow to achieve unprecedented sensitivity and selectivity. The combination of the ESENCE chip with an automated platform allows us to meet the WHO's ASSURED criteria. This platform promises to be an exciting POC candidate for early detection of periodontitis.
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Affiliation(s)
- Alexandra Griffith
- Department of Biomedical Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
| | - Charmi Chande
- Department of Biomedical Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
- Department of Chemical and Materials Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
| | - Sahitya Kulkarni
- Department of Biological Sciences, New Jersey Institute of Technology Newark NJ 07102 USA
| | - Josuel Morel
- Department of Biomedical Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
| | - Yu-Hsuan Cheng
- Department of Chemical and Materials Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
| | - Emi Shimizu
- Department of Endodontics, Rutgers School of Dental Medicine Newark NJ 07103 USA
- Department of Oral Biology, Rutgers School of Dental Medicine Newark NJ 07103 USA
| | - Carla Cugini
- Department of Oral Biology, Rutgers School of Dental Medicine Newark NJ 07103 USA
| | - Sagnik Basuray
- Department of Biomedical Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
- Department of Chemical and Materials Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
| | - Vivek Kumar
- Department of Biomedical Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
- Department of Chemical and Materials Engineering, New Jersey Institute of Technology Newark NJ 07102 USA
- Department of Biological Sciences, New Jersey Institute of Technology Newark NJ 07102 USA
- Department of Endodontics, Rutgers School of Dental Medicine Newark NJ 07103 USA
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Beser B, Reis T, Berber MN, Topaloglu E, Gungor E, Kılıc MC, Duman S, Çelik Ö, Kuran A, Bayrakdar IS. YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition. BMC Med Imaging 2024; 24:172. [PMID: 38992601 PMCID: PMC11238494 DOI: 10.1186/s12880-024-01338-w] [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: 04/13/2024] [Accepted: 06/17/2024] [Indexed: 07/13/2024] Open
Abstract
OBJECTIVES In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs. METHODS A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed. RESULTS The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation. CONCLUSIONS YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.
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Affiliation(s)
- Busra Beser
- Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
| | - Tugba Reis
- Pedodontics, Private Practice, Trabzon, Turkey
| | - Merve Nur Berber
- Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey
| | - Edanur Topaloglu
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Esra Gungor
- Department of Pedodontics, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Münevver Coruh Kılıc
- Department of Pedodontics, Faculty of Dentistry, Beykent University, Istanbul, Turkey
| | - Sacide Duman
- Department of Pedodontics, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alican Kuran
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, İzmit, Kocaeli, 41190, Turkey.
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [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: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Wang Y, Fu W, Zhang Y, Wang D, Gu Y, Wang W, Xu H, Ge X, Ye C, Fang J, Su L, Wang J, He W, Zhang X, Feng R. Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study. Sci Rep 2024; 14:14482. [PMID: 38914707 PMCID: PMC11196575 DOI: 10.1038/s41598-024-64893-w] [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: 12/01/2023] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.
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Affiliation(s)
- Yingwen Wang
- Nursing Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Weijia Fu
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Yuejie Zhang
- School of Computer Science, Fudan University, Shanghai, 200438, China
| | - Daoyang Wang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Ying Gu
- Nursing Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Weibing Wang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Hong Xu
- Nephrology Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Xiaoling Ge
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Chengjie Ye
- Medical Information Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Jinwu Fang
- School of Public, Health Fudan University, Shanghai, 200032, China
| | - Ling Su
- Statistical and Data Management Center, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Jiayu Wang
- National Health Commission Key Laboratory of Neonatal Diseases (Fudan University), Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Wen He
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, 201102, China
| | - Xiaobo Zhang
- Respiratory Department, Children's Hospital of Fudan University, Shanghai, 201102, China.
| | - Rui Feng
- School of Computer Science, Fudan University, Shanghai, 200438, China.
- School of Computer Science, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
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Sadegh-Zadeh SA, Bagheri M, Saadat M. Decoding children dental health risks: a machine learning approach to identifying key influencing factors. Front Artif Intell 2024; 7:1392597. [PMID: 38952410 PMCID: PMC11215085 DOI: 10.3389/frai.2024.1392597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/05/2024] [Indexed: 07/03/2024] Open
Abstract
Introduction and objectives This study investigates key factors influencing dental caries risk in children aged 7 and under using machine learning techniques. By addressing dental caries' prevalence, it aims to enhance early identification and preventative strategies for high-risk individuals. Methods Data from clinical examinations of 356 children were analyzed using Logistic Regression, Decision Trees, and Random Forests models. These models assessed the influence of dietary habits, fluoride exposure, and socio-economic status on caries risk, emphasizing accuracy, precision, recall, F1 score, and AUC metrics. Results Poor oral hygiene, high sugary diet, and low fluoride exposure were identified as significant caries risk factors. The Random Forest model demonstrated superior performance, illustrating the potential of machine learning in complex health data analysis. Our SHAP analysis identified poor oral hygiene, high sugary diet, and low fluoride exposure as significant caries risk factors. Conclusion Machine learning effectively identifies and quantifies dental caries risk factors in children. This approach supports targeted interventions and preventive measures, improving pediatric dental health outcomes. Clinical significance By leveraging machine learning to pinpoint crucial caries risk factors, this research lays the groundwork for data-driven preventive strategies, potentially reducing caries prevalence and promoting better dental health in children.
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Affiliation(s)
- Seyed-Ali Sadegh-Zadeh
- Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom
| | - Mahshid Bagheri
- Paediatric Dentistry, Population and Patient Health, King’s College London Dental Institute, London, United Kingdom
| | - Mozafar Saadat
- Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, United Kingdom
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Di Sarno L, Caroselli A, Tonin G, Graglia B, Pansini V, Causio FA, Gatto A, Chiaretti A. Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives. Biomedicines 2024; 12:1220. [PMID: 38927427 PMCID: PMC11200597 DOI: 10.3390/biomedicines12061220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.
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Affiliation(s)
- Lorenzo Di Sarno
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
| | - Anya Caroselli
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Giovanna Tonin
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Benedetta Graglia
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
| | - Valeria Pansini
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Francesco Andrea Causio
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Section of Hygiene and Public Health, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Antonio Gatto
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.T.); (V.P.)
| | - Antonio Chiaretti
- Department of Pediatrics, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy; (A.C.); (B.G.); (A.C.)
- The Italian Society of Artificial Intelligence in Medicine (SIIAM), 00165 Rome, Italy; (F.A.C.); (A.G.)
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Ba H, Zhang L, Yi Z. Enhancing clinical skills in pediatric trainees: a comparative study of ChatGPT-assisted and traditional teaching methods. BMC MEDICAL EDUCATION 2024; 24:558. [PMID: 38778332 PMCID: PMC11112818 DOI: 10.1186/s12909-024-05565-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND As artificial intelligence (AI) increasingly integrates into medical education, its specific impact on the development of clinical skills among pediatric trainees needs detailed investigation. Pediatric training presents unique challenges which AI tools like ChatGPT may be well-suited to address. OBJECTIVE This study evaluates the effectiveness of ChatGPT-assisted instruction versus traditional teaching methods on pediatric trainees' clinical skills performance. METHODS A cohort of pediatric trainees (n = 77) was randomly assigned to two groups; one underwent ChatGPT-assisted training, while the other received conventional instruction over a period of two weeks. Performance was assessed using theoretical knowledge exams and Mini-Clinical Evaluation Exercises (Mini-CEX), with particular attention to professional conduct, clinical judgment, patient communication, and overall clinical skills. Trainees' acceptance and satisfaction with the AI-assisted method were evaluated through a structured survey. RESULTS Both groups performed similarly in theoretical exams, indicating no significant difference (p > 0.05). However, the ChatGPT-assisted group showed a statistically significant improvement in Mini-CEX scores (p < 0.05), particularly in patient communication and clinical judgment. The AI-teaching approach received positive feedback from the majority of trainees, highlighting the perceived benefits in interactive learning and skill acquisition. CONCLUSION ChatGPT-assisted instruction did not affect theoretical knowledge acquisition but did enhance practical clinical skills among pediatric trainees. The positive reception of the AI-based method suggests that it has the potential to complement and augment traditional training approaches in pediatric education. These promising results warrant further exploration into the broader applications of AI in medical education scenarios.
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Affiliation(s)
- Hongjun Ba
- Department of Pediatric Cardiology, Heart Center, First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Road 2, Guangzhou, 510080, China.
- Key Laboratory on Assisted Circulation, Ministry of Health, 58# Zhongshan Road 2, Guangzhou, 510080, China.
| | - Lili Zhang
- Department of Pediatric Cardiology, Heart Center, First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Road 2, Guangzhou, 510080, China
| | - Zizheng Yi
- Department of Pediatric Cardiology, Heart Center, First Affiliated Hospital of Sun Yat-sen University, 58# Zhongshan Road 2, Guangzhou, 510080, China
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Elsonbaty S, Elgarba BM, Fontenele RC, Swaity A, Jacobs R. Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study. Int J Paediatr Dent 2024. [PMID: 38769619 DOI: 10.1111/ipd.13204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 04/25/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. AIM The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). DESIGN A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. RESULTS AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). CONCLUSION The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.
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Affiliation(s)
- Sara Elsonbaty
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Egyptian Ministry of Health and Population, Cairo, Egypt
| | - Bahaaeldeen M Elgarba
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Prosthodontics, Faculty of Dentistry, Tanta University, Tanta, Egypt
| | - Rocharles Cavalcante Fontenele
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Abdullah Swaity
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
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Warren S, Claman D, Meyer B, Peng J, Sezgin E. Acceptance of voice assistant technology in dental practice: A cross sectional study with dentists and validation using structural equation modeling. PLOS DIGITAL HEALTH 2024; 3:e0000510. [PMID: 38743686 DOI: 10.1371/journal.pdig.0000510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/15/2024] [Indexed: 05/16/2024]
Abstract
Voice assistant technologies (VAT) has been part of our daily lives, as a virtual assistant to complete requested tasks. The integration of VAT in dental offices has the potential to augment productivity and hygiene practices. Prior to the adoption of such innovations in dental settings, it is crucial to evaluate their applicability. This study aims to assess dentists' perceptions and the factors influencing their intention to use VAT in a clinical setting. A survey and research model were designed based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). The survey was sent to 7,544 Ohio-licensed dentists through email. The data was analyzed and reported using descriptive statistics, model reliability testing, and partial least squares regression (PLSR) to explain dentists' behavioral intention (BI) to use VAT. In total, 257 participants completed the survey. The model accounted for 74.2% of the variance in BI to use VAT. Performance expectancy and perceived enjoyment had significant positive influence on BI to use VAT. Perceived risk had significant negative influence on BI to use VAT. Self-efficacy had significantly influenced perceived enjoyment, accounting for 35.5% of the variance of perceived enjoyment. This investigation reveals that performance efficiency and user enjoyment are key determinants in dentists' decision to adopt VAT. Concerns regarding the privacy of VAT also play a crucial role in its acceptance. This study represents the first documented inquiry into dentists' reception of VAT, laying groundwork for future research and implementation strategies.
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Affiliation(s)
- Spencer Warren
- Department of Pediatric Dentistry, Nationwide Children's Hospital, Columbus, Ohio, United States of America
- Division of Pediatric Dentistry, The Ohio State University College of Dentistry, Columbus, Ohio, United States of America
| | - Daniel Claman
- Division of Pediatric Dentistry, The Ohio State University College of Dentistry, Columbus, Ohio, United States of America
| | - Beau Meyer
- Division of Pediatric Dentistry, The Ohio State University College of Dentistry, Columbus, Ohio, United States of America
| | - Jin Peng
- Information Technology Research & Innovation, Nationwide Children's Hospital, Columbus, Ohio, United States of America
| | - Emre Sezgin
- Center for Biobehavioral Health, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, United States of America
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
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10
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Spielman AI. Dental education and practice: past, present, and future trends. FRONTIERS IN ORAL HEALTH 2024; 5:1368121. [PMID: 38694791 PMCID: PMC11061397 DOI: 10.3389/froh.2024.1368121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
This position paper explores the historical transitions and current trends in dental education and practice and attempts to predict the future. Dental education and practice landscape, especially after the COVID-19 epidemic, are at a crossroads. Four fundamental forces are shaping the future: the escalating cost of education, the laicization of dental care, the corporatization of dental care, and technological advances. Dental education will likely include individualized, competency-based, asynchronous, hybrid, face-to-face, and virtual education with different start and end points for students. Dental practice, similarly, will be hybrid, with both face-to-face and virtual opportunities for patient care. Artificial intelligence will drive efficiencies in diagnosis, treatment, and office management.
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Affiliation(s)
- Andrew I. Spielman
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, NY, United States
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Ibraheem WI. Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Diagnostics (Basel) 2024; 14:806. [PMID: 38667452 PMCID: PMC11049199 DOI: 10.3390/diagnostics14080806] [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: 02/24/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was "What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.
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Affiliation(s)
- Wael I Ibraheem
- Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
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12
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Uribe SE, Maldupa I, Kavadella A, El Tantawi M, Chaurasia A, Fontana M, Marino R, Innes N, Schwendicke F. Artificial intelligence chatbots and large language models in dental education: Worldwide survey of educators. EUROPEAN JOURNAL OF DENTAL EDUCATION : OFFICIAL JOURNAL OF THE ASSOCIATION FOR DENTAL EDUCATION IN EUROPE 2024. [PMID: 38586899 DOI: 10.1111/eje.13009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/15/2024] [Accepted: 03/18/2024] [Indexed: 04/09/2024]
Abstract
INTRODUCTION Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. MATERIALS AND METHODS A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. RESULTS 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. CONCLUSION A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.
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Affiliation(s)
- Sergio E Uribe
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia
- Faculty of Dentistry, Universidad de Valparaiso, Valparaíso, Chile
- Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia
- ITU/WHO Focus Group AI on Health, Topic Group Dental, Geneva, Switzerland
| | - Ilze Maldupa
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia
| | - Argyro Kavadella
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
| | - Maha El Tantawi
- Faculty of Dentistry, Alexandria University, Alexandria, Egypt
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group AI on Health, Topic Group Dental, Geneva, Switzerland
- Department of Oral Medicine & Radiology, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Margherita Fontana
- Department of Cariology, Restorative Sciences and Endodontics, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Rodrigo Marino
- Melbourne Dental School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Nicola Innes
- School of Dentistry, College of Biomedical & Life Sciences, Cardiff University, Cardiff, UK
| | - Falk Schwendicke
- ITU/WHO Focus Group AI on Health, Topic Group Dental, Geneva, Switzerland
- Department of Conservative Dentistry and Periodontology, Ludwig-Maximilians-University Munich, Munich, Germany
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13
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Ong SH, Kim H, Song JS, Shin TJ, Hyun HK, Jang KT, Kim YJ. Fully automated deep learning approach to dental development assessment in panoramic radiographs. BMC Oral Health 2024; 24:426. [PMID: 38582843 PMCID: PMC10998373 DOI: 10.1186/s12903-024-04160-6] [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: 11/24/2023] [Accepted: 03/18/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Dental development assessment is an important factor in dental age estimation and dental maturity evaluation. This study aimed to develop and evaluate the performance of an automated dental development staging system based on Demirjian's method using deep learning. METHODS The study included 5133 anonymous panoramic radiographs obtained from the Department of Pediatric Dentistry database at Seoul National University Dental Hospital between 2020 and 2021. The proposed methodology involves a three-step procedure for dental staging: detection, segmentation, and classification. The panoramic data were randomly divided into training and validating sets (8:2), and YOLOv5, U-Net, and EfficientNet were trained and employed for each stage. The models' performance, along with the Grad-CAM analysis of EfficientNet, was evaluated. RESULTS The mean average precision (mAP) was 0.995 for detection, and the segmentation achieved an accuracy of 0.978. The classification performance showed F1 scores of 69.23, 80.67, 84.97, and 90.81 for the Incisor, Canine, Premolar, and Molar models, respectively. In the Grad-CAM analysis, the classification model focused on the apical portion of the developing tooth, a crucial feature for staging according to Demirjian's method. CONCLUSIONS These results indicate that the proposed deep learning approach for automated dental staging can serve as a supportive tool for dentists, facilitating rapid and objective dental age estimation and dental maturity evaluation.
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Affiliation(s)
- Seung-Hwan Ong
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Hyuntae Kim
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Ji-Soo Song
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Teo Jeon Shin
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Hong-Keun Hyun
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Ki-Taeg Jang
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Young-Jae Kim
- Department of Pediatric Dentistry, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
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14
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Hatia A, Doldo T, Parrini S, Chisci E, Cipriani L, Montagna L, Lagana G, Guenza G, Agosta E, Vinjolli F, Hoxha M, D’Amelio C, Favaretto N, Chisci G. Accuracy and Completeness of ChatGPT-Generated Information on Interceptive Orthodontics: A Multicenter Collaborative Study. J Clin Med 2024; 13:735. [PMID: 38337430 PMCID: PMC10856539 DOI: 10.3390/jcm13030735] [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: 01/10/2024] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Background: this study aims to investigate the accuracy and completeness of ChatGPT in answering questions and solving clinical scenarios of interceptive orthodontics. Materials and Methods: ten specialized orthodontists from ten Italian postgraduate orthodontics schools developed 21 clinical open-ended questions encompassing all of the subspecialities of interceptive orthodontics and 7 comprehensive clinical cases. Questions and scenarios were inputted into ChatGPT4, and the resulting answers were evaluated by the researchers using predefined accuracy (range 1-6) and completeness (range 1-3) Likert scales. Results: For the open-ended questions, the overall median score was 4.9/6 for the accuracy and 2.4/3 for completeness. In addition, the reviewers rated the accuracy of open-ended answers as entirely correct (score 6 on Likert scale) in 40.5% of cases and completeness as entirely correct (score 3 n Likert scale) in 50.5% of cases. As for the clinical cases, the overall median score was 4.9/6 for accuracy and 2.5/3 for completeness. Overall, the reviewers rated the accuracy of clinical case answers as entirely correct in 46% of cases and the completeness of clinical case answers as entirely correct in 54.3% of cases. Conclusions: The results showed a high level of accuracy and completeness in AI responses and a great ability to solve difficult clinical cases, but the answers were not 100% accurate and complete. ChatGPT is not yet sophisticated enough to replace the intellectual work of human beings.
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Affiliation(s)
- Arjeta Hatia
- Orthodontics Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (T.D.); (L.C.)
| | - Tiziana Doldo
- Orthodontics Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (T.D.); (L.C.)
| | - Stefano Parrini
- Oral Surgery Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy;
| | - Elettra Chisci
- Orthodontics Postgraduate School, University of Ferrara, 44121 Ferrara, Italy
| | - Linda Cipriani
- Orthodontics Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (T.D.); (L.C.)
| | - Livia Montagna
- Orthodontics Postgraduate School, University of Cagliari, 09121 Cagliari, Italy;
| | - Giuseppina Lagana
- Orthodontics Postgraduate School, “Sapienza” University of Rome, 00185 Rome, Italy;
| | - Guia Guenza
- Orthodontics Postgraduate School, University of Milano, 20019 Milan, Italy
| | - Edoardo Agosta
- Orthodontics Postgraduate School, University of Torino, 10024 Turin, Italy
| | - Franceska Vinjolli
- Orthodontics Postgraduate School, University of Roma Tor Vergata, 00133 Rome, Italy;
| | - Meladiona Hoxha
- Orthodontics Postgraduate School, “Cattolica” University of Rome, 00168 Rome, Italy;
| | - Claudio D’Amelio
- Orthodontics Postgraduate School, University of Chieti, 66100 Chieti, Italy;
| | - Nicolò Favaretto
- Orthodontics Postgraduate School, University of Trieste, 34100 Trieste, Italy
| | - Glauco Chisci
- Oral Surgery Postgraduate School, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy;
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15
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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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16
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Maltarollo TFH, Shitsuka C, Sivieri-Araújo G. ChatGPT and its use in paediatric dentistry. Eur Arch Paediatr Dent 2023; 24:675-676. [PMID: 37668870 DOI: 10.1007/s40368-023-00838-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 09/06/2023]
Affiliation(s)
- T F H Maltarollo
- Department of Preventive and Restorative Dentistry, School of Dentistry, São Paulo State University (Unesp), Araçatuba, Brazil
| | - C Shitsuka
- Department of Pediatric Dentistry, School of Dentistry, Faculdades Metropolitanas Unidas, São Paulo, Brazil.
| | - G Sivieri-Araújo
- Department of Preventive and Restorative Dentistry, School of Dentistry, São Paulo State University (Unesp), Araçatuba, Brazil
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17
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Anil S, Porwal P, Porwal A. Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques. Cureus 2023; 15:e41694. [PMID: 37575741 PMCID: PMC10413921 DOI: 10.7759/cureus.41694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
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Affiliation(s)
| | - Priyanka Porwal
- Dentistry, Pushpagiri Institute of Medical Sciences and Research Centre, Tiruvalla, IND
| | - Amit Porwal
- Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan, SAU
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18
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Karan-Romero M, Salazar-Gamarra RE, Leon-Rios XA. Evaluation of Attitudes and Perceptions in Students about the Use of Artificial Intelligence in Dentistry. Dent J (Basel) 2023; 11:dj11050125. [PMID: 37232776 DOI: 10.3390/dj11050125] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND The implementation of artificial intelligence brings with it a great change in health care, however, there is a discrepancy about the perceptions and attitudes that dental students present towards these new technologies. METHODS The study design was observational, descriptive, and cross-sectional. A total of 200 dental students who met the inclusion criteria were surveyed online. For the qualitative variables, descriptive statistical measures were obtained, such as absolute and relative frequencies. For the comparison of the main variables with the type of educational institution, sex and level of education, the chi-square test or Fisher's exact test was used according to the established assumptions with a level of statistical significance of p < 0.05 and a confidence level of 95%. RESULTS The results indicated that 86% of the students surveyed agreed that artificial intelligence will lead to great advances in dentistry. However, 45% of the participants disagreed that artificial intelligence would replace dentists in the future. In addition, the respondents agreed that the use of artificial intelligence should be part of undergraduate and postgraduate studies with 67% and 72% agreement rates respectively. CONCLUSION The attitudes and perceptions of the students indicate that 86% agreed that artificial intelligence will lead to great advances in dentistry. This suggests a bright future for the relationship between dentists and artificial intelligence.
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Affiliation(s)
- Milan Karan-Romero
- School of Dentistry, Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Av. Alameda San Marcos, 11, Chorrillos 15067, Lima, Peru
| | | | - Ximena Alejandra Leon-Rios
- School of Dentistry, Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Av. Alameda San Marcos, 11, Chorrillos 15067, Lima, Peru
- CTS 367 Research Group, Andalusian Research Plan, Junta de Andalucía (Spain), University of Granada, 18071 Granada, Spain
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Alhaidry HM, Fatani B, Alrayes JO, Almana AM, Alfhaed NK. ChatGPT in Dentistry: A Comprehensive Review. Cureus 2023; 15:e38317. [PMID: 37266053 PMCID: PMC10230850 DOI: 10.7759/cureus.38317] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2023] [Indexed: 06/03/2023] Open
Abstract
Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence chatbot that uses natural language processing that can respond to human input in a conversational manner. ChatGPT has numerous applications in the health care system including dentistry; it is used in diagnoses and for assessing disease risk and scheduling appointments. It also has a role in scientific research. In the dental field, it has provided many benefits such as detecting dental and maxillofacial abnormalities on panoramic radiographs and identifying different dental restorations. Therefore, it helps in decreasing the workload. But even with these benefits, one should take into consideration the risks and limitations of this chatbot. Few articles mentioned the use of ChatGPT in dentistry. This comprehensive review represents data collected from 66 relevant articles using PubMed and Google Scholar as databases. This review aims to discuss all relevant published articles on the use of ChatGPT in dentistry.
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Affiliation(s)
- Hind M Alhaidry
- Advanced General Dentistry, Prince Sultan Military Medical City, Riyadh, SAU
| | - Bader Fatani
- Dentistry, College of Dentistry, King Saud University, Riyadh, SAU
| | - Jenan O Alrayes
- Dentistry, College of Dentistry, King Saud University, Riyadh, SAU
| | | | - Nawaf K Alfhaed
- Dentistry, College of Dentistry, King Saud University, Riyadh, SAU
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