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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:641-655. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [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: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Carvalho BKG, Nolden EL, Wenning AS, Kiss-Dala S, Agócs G, Róth I, Kerémi B, Géczi Z, Hegyi P, Kivovics M. Diagnostic Accuracy of Artificial Intelligence for Approximal Caries on Bitewing Radiographs: A Systematic Review and Meta-analysis. J Dent 2024:105388. [PMID: 39396775 DOI: 10.1016/j.jdent.2024.105388] [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: 06/25/2024] [Revised: 09/13/2024] [Accepted: 10/01/2024] [Indexed: 10/15/2024] Open
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs. METHODS This study included randomized controlled trials (RCTs) and non-randomized controlled trials (non-RCTs) reporting on the diagnostic accuracy of AI for approximal carious lesions on bitewing radiographs. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A systematic search was conducted on November 4, 2023, in PubMed, Cochrane, and Embase databases and an updated search was performed on August 28, 2024. The primary outcomes assessed were sensitivity, specificity, and overall accuracy. Sensitivity and specificity were pooled using a bivariate model. RESULTS Of the 2,442 studies identified, 21 met the inclusion criteria. The pooled sensitivity and specificity of AI were 0.94 (confidence interval (CI): ± 0.78-0.99) and 0.91 (CI: ± 0.84-0.95), respectively. The positive predictive value (PPV) ranged from 0.15 to 0.87, indicating a moderate capacity for identifying true positives among decayed teeth. The negative predictive value (NPV) ranged from 0.79 to 1.00, demonstrating a high ability to exclude healthy teeth. The diagnostic odds ratio was high, indicating strong overall diagnostic performance. CONCLUSIONS AI models demonstrate clinically acceptable diagnostic accuracy for approximal caries on bitewing radiographs. Although AI can be valuable for preliminary screening, positive findings should be verified by dental experts to prevent unnecessary treatments and ensure timely diagnosis. AI models are highly reliable in excluding healthy approximal surfaces. CLINICAL SIGNIFICANCE AI can assist dentists in detecting approximal caries on bitewing radiographs. However, expert supervision is required to prevent iatrogenic damage and ensure timely diagnosis.
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Affiliation(s)
| | - Elias-Leon Nolden
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary.
| | - Alexander Schulze Wenning
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary.
| | - Szilvia Kiss-Dala
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary.
| | - Gergely Agócs
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary; Department of Biophysics and Radiation Biology, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary.
| | - Ivett Róth
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary; Department of Prosthodontics, Semmelweis University, Szentkirályi utca 47, 1088, Budapest, Hungary.
| | - Beáta Kerémi
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary; Department of Restorative Dentistry and Endodontics, Semmelweis University, Szentkirályi utca 47, 1088, Budapest, Hungary.
| | - Zoltán Géczi
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary; Department of Prosthodontics, Semmelweis University, Szentkirályi utca 47, 1088, Budapest, Hungary.
| | - Péter Hegyi
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary; Institute of Pancreatic Diseases, Semmelweis University, Tömő utca 25-29, 1083, Budapest, Hungary; Institute for Translational Medicine, Medical School, University of Pécs, Szigeti utca 12, 7624, Pécs, Hungary.
| | - Márton Kivovics
- Centre for Translational Medicine, Semmelweis University, Tűzoltó utca 37-47, 1072, Budapest, Hungary; Department of Community Dentistry, Semmelweis University, Szentkirályi utca 40, 1088, Budapest, Hungary.
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Al-Khalifa KS, Ahmed WM, Azhari AA, Qaw M, Alsheikh R, Alqudaihi F, Alfaraj A. The Use of Artificial Intelligence in Caries Detection: A Review. Bioengineering (Basel) 2024; 11:936. [PMID: 39329679 PMCID: PMC11428802 DOI: 10.3390/bioengineering11090936] [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: 07/07/2024] [Revised: 08/20/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice.
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Affiliation(s)
- Khalifa S. Al-Khalifa
- Department of Preventive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Walaa Magdy Ahmed
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (W.M.A.); (A.A.A.)
| | - Amr Ahmed Azhari
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (W.M.A.); (A.A.A.)
| | - Masoumah Qaw
- Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (M.Q.); (R.A.)
| | - Rasha Alsheikh
- Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (M.Q.); (R.A.)
| | - Fatema Alqudaihi
- Department of Restorative Dentistry, Khobar Dental Complex, Eastern Health Cluster, Dammam 32253, Saudi Arabia;
| | - Amal Alfaraj
- Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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Modesto-Mata M, de la Fuente Valentín L, Hlusko LJ, Martínez de Pinillos M, Towle I, García-Campos C, Martinón-Torres M, Bermúdez de Castro JM. Artificial neural networks reconstruct missing perikymata in worn teeth. Anat Rec (Hoboken) 2024; 307:3120-3138. [PMID: 38468123 DOI: 10.1002/ar.25416] [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: 08/01/2023] [Revised: 02/08/2024] [Accepted: 02/11/2024] [Indexed: 03/13/2024]
Abstract
Dental evolutionary studies in hominins are key to understanding how our ancestors and close fossil relatives grew from the early stages of embryogenesis into adults. In a sense, teeth are like an airplane's 'black box' as they record important variables for assessing developmental timing, enabling comparisons within and between populations, species, and genera. The ability to discern this type of nuanced information is embedded in the nature of how tooth enamel and dentin form: incrementally and over years. This incremental growth leaves chronological indicators in the histological structure of enamel, visible on the crown surface as perikymata. These structures are used in the process of reconstructing the rate and timing of tooth formation. Unfortunately, the developmentally earliest growth lines in lateral enamel are quickly lost to wear once the tooth crown erupts. We developed a method to reconstruct these earliest, missing perilymata from worn teeth through knowledge of the later-developed, visible perikymata for all tooth types (incisors, canines, premolars, and molars) using a modern human dataset. Building on our previous research using polynomial regressions, here we describe an artificial neural networks (ANN) method. This new ANN method mostly predicts within 2 counts the number of perikymata present in each of the first three deciles of the crown height for all tooth types. Our ANN method for estimating perikymata lost through wear has two immediate benefits: more accurate values can be produced and worn teeth can be included in dental research. This tool is available on the open-source platform R within the package teethR released under GPL v3.0 license, enabling other researchers the opportunity to expand their datasets for studies of periodicity in histological growth, dental development, and evolution.
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Affiliation(s)
- Mario Modesto-Mata
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Universidad Internacional de La Rioja (UNIR), Logroño (La Rioja), Spain
| | | | - Leslea J Hlusko
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
| | - Marina Martínez de Pinillos
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Laboratorio de Evolución Humana (LEH), Universidad de Burgos, Burgos, Spain
| | - Ian Towle
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
| | - Cecilia García-Campos
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Facultad de Ciencias, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, Spain
| | - María Martinón-Torres
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Department of Anthropology, University College London, London, UK
| | - José María Bermúdez de Castro
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Department of Anthropology, University College London, London, UK
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Szabó V, Szabó BT, Orhan K, Veres DS, Manulis D, Ezhov M, Sanders A. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs. J Dent 2024; 147:105105. [PMID: 38821394 DOI: 10.1016/j.jdent.2024.105105] [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: 10/10/2023] [Revised: 05/21/2024] [Accepted: 05/28/2024] [Indexed: 06/02/2024] Open
Abstract
OBJECTIVES This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. METHODS The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated. RESULTS During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. CONCLUSIONS The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. CLINICAL SIGNIFICANCE Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology..
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Affiliation(s)
- Viktor Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary
| | - Bence Tamás Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary.
| | - Kaan Orhan
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey; Medical Design Application, and Research Center (MEDITAM), Ankara University, Ankara, Turkey
| | - Dániel Sándor Veres
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, Hungary
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Asci E, Kilic M, Celik O, Cantekin K, Bircan HB, Bayrakdar İS, Orhan K. A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition. CHILDREN (BASEL, SWITZERLAND) 2024; 11:690. [PMID: 38929269 PMCID: PMC11202197 DOI: 10.3390/children11060690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/03/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024]
Abstract
OBJECTIVES The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. METHODS This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. RESULTS In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. CONCLUSIONS Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.
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Affiliation(s)
- Esra Asci
- Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey; (E.A.); (H.B.B.)
| | - Munevver Kilic
- Department of Pediatric Dentistry, Faculty of Dentistry, Beykent University, İstanbul 34398, Turkey
| | - Ozer Celik
- Department of Mathematics Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
| | - Kenan Cantekin
- Department of Pediatric Dentistry, Faculty of Dentistry, Sakarya University, Sakarya 54050, Turkey;
| | - Hasan Basri Bircan
- Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey; (E.A.); (H.B.B.)
| | - İbrahim Sevki Bayrakdar
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06620, Turkey;
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Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review. Bioengineering (Basel) 2024; 11:431. [PMID: 38790300 PMCID: PMC11118054 DOI: 10.3390/bioengineering11050431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.
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Affiliation(s)
| | - Shayan Darvish
- School of Dentistry, University of Michigan, Ann Arbor, MI 48104, USA;
| | - Bahareh Nazemi Salman
- Department of Pediatric Dentistry, School of Dentistry, Zanjan University of Medical Sciences, Zanjan 4513956184, Iran
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
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Pringle AJ, Kumaran V, Missier MS, Nadar ASP. Perceptiveness and Attitude on the use of Artificial Intelligence (AI) in Dentistry among Dentists and Non-Dentists - A Regional Survey. JOURNAL OF PHARMACY AND BIOALLIED SCIENCES 2024; 16:S1481-S1486. [PMID: 38882768 PMCID: PMC11174187 DOI: 10.4103/jpbs.jpbs_1019_23] [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: 10/10/2023] [Revised: 10/14/2023] [Accepted: 10/22/2023] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) is an emerging tool in modern medicine and the digital world. AI can help dentists diagnose oral diseases, design treatment plans, monitor patient progress and automate administrative tasks. The aim of this study is to evaluate the perception and attitude on use of artificial intelligence in dentistry for diagnosis and treatment planning among dentists and non-dentists' population of south Tamil Nadu region in India. Materials and Methods A cross sectional online survey conducted using 20 close ended questionnaire google forms which were circulated among the dentists and non -dentists population of south Tamil Nadu region in India. The data collected from 264 participants (dentists -158, non-dentists -106) within a limited time frame were subjected to descriptive statistical analysis. Results 70.9% of dentists are aware of artificial intelligence in dentistry. 40.5% participants were not aware of AI in caries detection but aware of its use in interpretation of radiographs (43.9%) and in planning of orthognathic surgery (42.4%) which are statistically significant P < 0.05.44.7% support clinical experience of a human doctor better than AI diagnosis. Dentists of 54.4% agree to support AI use in dentistry. Conclusion The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non -dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning.
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Affiliation(s)
- A Jebilla Pringle
- Department of Orthodontics, Rajas Dental College and Hospitals, Kavalkinaru, Tamil Nadu, India
| | - V Kumaran
- Department of Orthodontics, J.K.K. Nataraja Dental College and Hospitals, Nammakal, Tamil Nadu, India
| | - Mary Sheloni Missier
- Department of Orthodontics, Rajas Dental College and Hospitals, Kavalkinaru, Tamil Nadu, India
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Pérez de Frutos J, Holden Helland R, Desai S, Nymoen LC, Langø T, Remman T, Sen A. AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study. BMC Oral Health 2024; 24:344. [PMID: 38494481 PMCID: PMC10946166 DOI: 10.1186/s12903-024-04120-0] [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: 09/30/2023] [Accepted: 03/07/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. METHODS A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dental clinicians, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. RESULTS The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively. CONCLUSION Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images.
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Affiliation(s)
- Javier Pérez de Frutos
- Department of Health Research, SINTEF Digital, Professor Brochs gate 2, Trondheim, 7030, Norway.
| | - Ragnhild Holden Helland
- Department of Health Research, SINTEF Digital, Professor Brochs gate 2, Trondheim, 7030, Norway
| | | | - Line Cathrine Nymoen
- Department of public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Kompetansesenteret Tannhelse Midt (TkMidt), Trondheim, Norway
| | - Thomas Langø
- Department of Health Research, SINTEF Digital, Professor Brochs gate 2, Trondheim, 7030, Norway
| | | | - Abhijit Sen
- Department of public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Kompetansesenteret Tannhelse Midt (TkMidt), Trondheim, Norway
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Hamidi O, Afrasiabi M, Namaki M. GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses. BMC Med Res Methodol 2024; 24:50. [PMID: 38413856 PMCID: PMC10898185 DOI: 10.1186/s12874-024-02183-9] [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: 11/09/2023] [Accepted: 02/18/2024] [Indexed: 02/29/2024] Open
Abstract
INTRODUCTION The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep learning (DL) model optimized by an evolutionary algorithm (genetic algorithm/GA) to determine sex and age using paranasal sinus parameters based on cone-beam computed tomography (CBCT). METHODS Two hundred and forty CBCT images (including 129 females and 111 males, aged 18-52) were included in this study. CBCT images were captured using the Newtom3G device with specific exposure parameters. These images were then analyzed in ITK-SNAP 3.6.0 beta software to extract four paranasal sinus parameters: height, width, length, and volume for both the frontal and maxillary sinuses. A hybrid model, Genetic Algorithm-Deep Neural Network (GADNN), was proposed for feature selection and classification. Traditional statistical methods and machine learning models, including logistic regression (LR), random forest (RF), multilayer perceptron neural network (MLP), and deep learning (DL) were evaluated for their performance. The synthetic minority oversampling technique was used to deal with the unbalanced data. RESULTS GADNN showed superior accuracy in both sex determination (accuracy of 86%) and age determination (accuracy of 68%), outperforming other models. Also, DL and RF were the second and third superior methods in sex determination (accuracy of 78% and 71% respectively) and age determination (accuracy of 92% and 57%). CONCLUSIONS The study introduces a novel approach combining DL and GA to enhance sex determination and age determination accuracy. The potential of DL in forensic dentistry is highlighted, demonstrating its efficiency in improving accuracy for sex determination and age determination. The study contributes to the burgeoning field of DL in dentistry and forensic sciences.
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Affiliation(s)
- Omid Hamidi
- Department of Science, Hamedan University of Technology, Hamedan, Iran
| | - Mahlagha Afrasiabi
- Department of Computer Engineering, Hamedan University of Technology, Hamedan, Iran.
| | - Marjan Namaki
- Department of Computer Engineering, Hamedan University of Technology, Hamedan, Iran
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Albano D, Galiano V, Basile M, Di Luca F, Gitto S, Messina C, Cagetti MG, Del Fabbro M, Tartaglia GM, Sconfienza LM. Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review. BMC Oral Health 2024; 24:274. [PMID: 38402191 PMCID: PMC10894487 DOI: 10.1186/s12903-024-04046-7] [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: 11/11/2023] [Accepted: 02/17/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). MATERIALS AND METHODS An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. RESULTS Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3-0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. CONCLUSION AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. PROTOCOL PROSPERO identifier: CRD42023470708.
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Affiliation(s)
- Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
| | | | - Mariachiara Basile
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Filippo Di Luca
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Maria Grazia Cagetti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Massimo Del Fabbro
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Gianluca Martino Tartaglia
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
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Chen Z, Liu Y, Xie X, Deng F. Influence of bone density on the accuracy of artificial intelligence-guided implant surgery: An in vitro study. J Prosthet Dent 2024; 131:254-261. [PMID: 35469649 DOI: 10.1016/j.prosdent.2021.07.019] [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: 01/10/2021] [Revised: 07/10/2021] [Accepted: 07/12/2021] [Indexed: 11/27/2022]
Abstract
STATEMENT OF PROBLEM Artificial intelligence (AI) has been found to be applicable in medical tests and diagnostics. However, studies on the application of AI technology in oral implantology are lacking. In addition, whether bone density affects the accuracy of guided implant surgery has not been determined. PURPOSE The purpose of this in vitro study was to determine the clinical reliability of an AI-assisted implant planning software program with an in vitro model. An additional goal was to determine the effect of bone density on the accuracy of static computer-assisted implant surgery (CAIS). MATERIAL AND METHODS Ten participants with missing mandibular left first molars were selected for analysis, and surgical fully guided templates were designed by using an AI implant planning software program. Jaw models were produced in 3 filling rate groups (group L: 25%; group M: 40%; group H: 55%, higher filling rate with representatives of the denser simulated bone density) by 3-dimensional (3D) printing. The preoperative and postoperative positions of the implants were compared by measuring the value of deviation through oral scanning. The mean 3D shoulder and apical and angular deviations were calculated for each group. The data were analyzed using 1-way ANOVA (α=.05 corrected for multiple testing by using Bonferroni-Holm adjustment). RESULTS The mean ±standard deviation 3D shoulder and apical and angular deviations were 0.80 ±0.32 mm, 1.43 ±0.47 mm, and 3.68 ±1.30 degrees. These values were lower than the clinical safety distance of the fully guided implant template. A significantly lower mean 3D apical deviation (1.12 ±0.33 mm, P=.023) and angular deviation (2.81 ±1.11 degrees, P=.018) were observed in group L than in group H (1.68 ±0.37 mm, 4.32 ±0.99 degrees). However, no significant differences were found among the 3 groups in 3D deviation at the shoulder (P>.05). CONCLUSIONS AI implant planning software program could design the ideal implant position through self-learning. The accuracy of the AI-assisted designed implant template in this study indicated its clinical reliability. Higher bone density led to increased implant deviations.
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Affiliation(s)
- Zhicong Chen
- Graduate student, Department of Oral Implantology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, Guangdong, PR China
| | - Yun Liu
- Doctor, Department of Oral Implantology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, Guangdong, PR China
| | - Xin Xie
- Undergraduate, Department of Oral Implantology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, Guangdong, PR China
| | - Feilong Deng
- Professor, Department of Oral Implantology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-Sen University, Guangzhou, Guangdong, PR China.
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Amasya H, Alkhader M, Serindere G, Futyma-Gąbka K, Aktuna Belgin C, Gusarev M, Ezhov M, Różyło-Kalinowska I, Önder M, Sanders A, Costa ALF, de Castro Lopes SLP, Orhan K. Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging. Diagnostics (Basel) 2023; 13:3471. [PMID: 37998607 PMCID: PMC10669958 DOI: 10.3390/diagnostics13223471] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/12/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.
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Affiliation(s)
- Hakan Amasya
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye;
- CAST (Cerrahpasa Research, Simulation and Design Laboratory), Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul 34220, Türkiye
| | - Mustafa Alkhader
- Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Gözde Serindere
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Mustafa Kemal University, Hatay 31060, Türkiye; (G.S.); (C.A.B.)
| | - Karolina Futyma-Gąbka
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland; (K.F.-G.); or (I.R.-K.)
| | - Ceren Aktuna Belgin
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Mustafa Kemal University, Hatay 31060, Türkiye; (G.S.); (C.A.B.)
| | - Maxim Gusarev
- Diagnocat, Inc., San Francisco, CA 94102, USA; (M.G.); (M.E.); (A.S.)
| | - Matvey Ezhov
- Diagnocat, Inc., San Francisco, CA 94102, USA; (M.G.); (M.E.); (A.S.)
| | - Ingrid Różyło-Kalinowska
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland; (K.F.-G.); or (I.R.-K.)
| | - Merve Önder
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 0600, Türkiye;
| | - Alex Sanders
- Diagnocat, Inc., San Francisco, CA 94102, USA; (M.G.); (M.E.); (A.S.)
| | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 08060-070, SP, Brazil;
| | - Sérgio Lúcio Pereira de Castro Lopes
- Science and Technology Institute, Department of Diagnosis and Surgery, São Paulo State University (UNESP), São José dos Campos 01049-010, SP, Brazil;
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 0600, Türkiye;
- Research Center (MEDITAM), Ankara University Medical Design Application, Ankara 06560, Türkiye
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, 1088 Budapest, Hungary
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Chawla RL, Gadge NP, Ronad S, Waghmare A, Patil A, Deshmukh G. Knowledge, Attitude and Perception Regarding Artificial Intelligence in Periodontology: A Questionnaire Study. Cureus 2023; 15:e48309. [PMID: 38058340 PMCID: PMC10697475 DOI: 10.7759/cureus.48309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2023] [Indexed: 12/08/2023] Open
Abstract
INTRODUCTION The utilization of artificial intelligence (AI) and machine learning (ML) models has brought about a significant transformation in the manner in which periodontists gather information, evaluate associated risks, develop diverse treatment alternatives, anticipate and diagnose dental conditions that compromise periodontal health. The principal objective of this prospective study was to examine periodontists' understanding and acceptance of the application of AI in the realm of periodontology. MATERIALS AND METHODS This observational study was conducted on 275 participants based on questionnaire using Google Forms. These forms were pre-validated and subsequently circulated among periodontists in Maharashtra via various social media platforms. The study, in its entirety, comprised four open-ended questions on participants' demographics and 14 closed-ended questions, all of which were presented to the participants in English. These questions aimed to elicit participants' awareness, knowledge, attitudes, and perspectives regarding emerging applications of AI in the field of periodontology. To analyze the collected data, researchers employed the widely utilized Statistical Package for Social Sciences (SPSS) version 22.0. RESULT A 75% response rate was achieved and 68% of the respondents were female. 62% periodontists were aware of AI; however, only 24% were aware of its working principles. Most respondents agreed with the use of AI in periodontal diagnosis; however, they disagreed with the use of AI in predicting clinical attachment loss (69%). 80-82% respondents felt that AI should be a part of postgraduate training and should be implemented in clinical practice. However, most periodontists do not use AI for diagnostic or research purposes. 49% periodontists felt that AI does not have better diagnostic accuracy than periodontists, and therefore cannot replace them in the future. CONCLUSION Most periodontists possessed a reasonable level of understanding regarding the utilization of AI in the domain of periodontology and expressed a desire to incorporate it into their diagnostic and treatment planning processes for periodontal conditions. Additional endeavors must be undertaken to enhance periodontists' awareness concerning the effective implementation of AI within their professional practice, with the aim of facilitating personalized treatment planning for their respective patients. It is postulated that the integration of AI will augment the likelihood of achieving favorable outcomes within the realm of periodontology.
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Affiliation(s)
- Ruhee L Chawla
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Nidhi P Gadge
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Sunil Ronad
- Prosthodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Alka Waghmare
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
| | - Aarti Patil
- Periodontics, Jawahar Medical Foundation ACPM Dental College, Dhule, IND
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Alzaid N, Ghulam O, Albani M, Alharbi R, Othman M, Taher H, Albaradie S, Ahmed S. Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties. Cureus 2023; 15:e47033. [PMID: 37965397 PMCID: PMC10642940 DOI: 10.7759/cureus.47033] [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] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Since the beginning of recorded history, the human brain has been one of the most intriguing structures for scientists and engineers. Over the centuries, newer technologies have been developed based on principles that seek to mimic their functioning, but the creation of a machine that can think and behave like a human remains an unattainable fantasy. This idea is now known as "artificial intelligence". Dentistry has begun to experience the effects of artificial intelligence (AI). These include image enhancement for radiology, which improves the visibility of dental structures and facilitates disease diagnosis. AI has also been utilized for the identification of periapical lesions and root anatomy in endodontics, as well as for the diagnosis of periodontitis. This review is intended to provide a comprehensive overview of the use of AI in modern dentistry's numerous specialties. The relevant publications published between March 1987 and July 2023 were identified through an exhaustive search. Studies published in English were selected and included data regarding AI applications among various dental specialties. Dental practice involves more than just disease diagnosis, including correlation with clinical findings and administering treatment to patients. AI cannot replace dentists. However, a comprehensive understanding of AI concepts and techniques will be advantageous in the future. AI models for dental applications are currently being developed.
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Affiliation(s)
- Najd Alzaid
- Dentistry, University of Hail College of Dentistry, Hail, SAU
| | - Omar Ghulam
- General Dentistry, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Modhi Albani
- Dentistry, University of Hail College of Dentistry, Hail, SAU
| | - Rafa Alharbi
- Dentistry, Taibah University College of Dentistry, Madinah, SAU
| | - Mayan Othman
- Dentistry, Taibah University College of Dentistry, Madinah, SAU
| | - Hasan Taher
- Endodontics, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Saleem Albaradie
- General Dentistry, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Suhael Ahmed
- Maxillofacial Surgery and Diagnostic Sciences, College of Medicine and Dentistry, Riyadh Elm University, Riyadh, SAU
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16
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Patil S, Joda T, Soffe B, Awan KH, Fageeh HN, Tovani-Palone MR, Licari FW. Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases: A systematic review. J Am Dent Assoc 2023; 154:795-804.e1. [PMID: 37452813 DOI: 10.1016/j.adaj.2023.05.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/13/2023] [Accepted: 05/17/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Artificial intelligence (AI) can aid in the diagnosis and treatment planning of periodontal disease by means of reducing subjectivity. This systematic review aimed to evaluate the efficacy of AI models in detecting radiographic periodontal bone loss (PBL) and accuracy in classifying lesions. TYPES OF STUDIES REVIEWED The authors conducted an electronic search of PubMed, Scopus, and Web of Science for articles published through August 2022. Articles evaluating the efficacy of AI in determining PBL were included. The authors assessed the articles using the Quality Assessment for Studies of Diagnostic Accuracy tool. They used the Grading of Recommendations Assessment, Development and Evaluation criteria to evaluate the certainty of evidence. RESULTS Of the 13 articles identified through electronic search, 6 studies met the inclusion criteria, using a variety of AI algorithms and different modalities, including panoramic and intraoral radiographs. Sensitivity, specificity, accuracy, and pixel accuracy were the outcomes measured. Although some studies found no substantial difference between AI and dental clinicians' performance, others showed AI's superiority in detecting PBL. Evidence suggests that AI has the potential to aid in the detection of PBL and classification of periodontal diseases. However, further research is needed to standardize AI algorithms and validate their clinical usefulness. PRACTICAL IMPLICATIONS Although the use of AI may offer some benefits in the detection and classification of periodontal diseases, the low level of evidence and the inconsistent performance of AI algorithms suggest that caution should be exercised when considering the use of AI models in diagnosing PBL. This review was registered at PROSPERO (CRD42022364600).
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Ahmed WM, Azhari AA, Fawaz KA, Ahmed HM, Alsadah ZM, Majumdar A, Carvalho RM. Artificial intelligence in the detection and classification of dental caries. J Prosthet Dent 2023:S0022-3913(23)00478-X. [PMID: 37640607 DOI: 10.1016/j.prosdent.2023.07.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
STATEMENT OF PROBLEM Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. PURPOSE The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. MATERIAL AND METHODS Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. RESULTS The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. CONCLUSIONS The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care.
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Affiliation(s)
- Walaa Magdy Ahmed
- Assistant Professor, Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Amr Ahmed Azhari
- Assistant Professor, Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Khaled Ahmed Fawaz
- Associate Professor, Department of Orthopedic Surgery, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Hani M Ahmed
- Assistant Professor, Department of Civil Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Zainab M Alsadah
- Consultant in Restorative Dentistry, Dental Department, East Jeddah General Hospital, Ministry of Health, Jeddah, Saudi Arabia
| | - Aritra Majumdar
- Graduate student, Department of Computer Science, Computer Science and Applications, Virginia Polytechnic Institute and State University, Blacksburg, Va
| | - Ricardo Marins Carvalho
- Professor, Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, British Columbia, Canada
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Altukroni A, Alsaeedi A, Gonzalez-Losada C, Lee JH, Alabudh M, Mirah M, El-Amri S, Ezz El-Deen O. Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs. BMC Oral Health 2023; 23:553. [PMID: 37563659 PMCID: PMC10416487 DOI: 10.1186/s12903-023-03251-0] [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: 02/26/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners' lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists. METHODS This study was a diagnostic, multi-centric study, with 3461 digital periapical radiographs from three countries and seven centers. MSc was built using Yolov5-x model, and it was used for exposed and unexposed pulp detection. The dataset was split into a train, validate, and test dataset; the ratio was 8-1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right diagnosis exposed (RDE), right diagnosis not exposed (RDNE), false diagnosis exposed (FDE), false diagnosis not exposed (FDNE), missed diagnosis (MD), and over diagnosis (OD). RESULTS MSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and AUC of 0.956 (P<.05). The results showed a higher mean of 1.94 for all right (correct) diagnosis parameters in MSc group, while a higher mean of 0.64 for all wrong diagnosis parameters in the dentists group (P<.05). CONCLUSIONS The designed MSc tool proved itself reliable in the detection and differentiating between exposed and unexposed pulp in the internally validated model. It also showed a better performance for the detection of exposed and unexposed pulp when compared to the 10 dentists' consensus.
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Affiliation(s)
| | - A Alsaeedi
- Department of Computer Science, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia
| | - C Gonzalez-Losada
- School of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - J H Lee
- Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea
| | - M Alabudh
- Ministry of Health, Medina, Saudi Arabia
| | - M Mirah
- Department of Dental Materials, Taibah University, Medina, Saudi Arabia
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Cholan P, Ramachandran L, Umesh SG, P S, Tadepalli A. The Impetus of Artificial Intelligence on Periodontal Diagnosis: A Brief Synopsis. Cureus 2023; 15:e43583. [PMID: 37719493 PMCID: PMC10503663 DOI: 10.7759/cureus.43583] [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] [Accepted: 08/16/2023] [Indexed: 09/19/2023] Open
Abstract
The current advances in digitized data additions, machine learning and computing framework, lead to the swiftly emerging concept of "Artificial Intelligence" (AI), that are developing into areas that were formerly contemplated for human expertise. AI is a relatively rapid paced mechanics wherein the computer technology is tuned to perform human tasks. An auxiliary domain of AI is machine learning (ML), and Deep learning, a subclass of ML technique comprehends multi-layer mathematical operations. AI-based applications have tremendous potential to improve and systematize patient care thereby alleviating dentists from laborious regular tasks, and facilitate personalized, predictive and preventive dentistry. In the dental clinic, AI can execute a variety of easy tasks with greater accuracy, minimal manpower, and with fewer mistakes over human equivalents. These tasks range from appointment scheduling and coordination to helping with clinical evaluation and therapy. Besides, this could assist in the early diagnosis of dental and maxillofacial abnormalities like periodontal ailments, root caries, bony lesions, and facial malformations in addition to automatically identifying and classifying dental restorations on digital radiographs. This brusque narrative review describes the AI-based systems, their respective applications in periodontal diagnosis, the multifarious studies, possible limitations and the predictable future of AI-based dental diagnostics and treatment planning.
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Affiliation(s)
- Priyanka Cholan
- Periodontics, Sri Ramaswamy Memorial (SRM) Dental College & Hospital, Chennai, IND
| | - Lakshmi Ramachandran
- Periodontics & Oral Implantology, Sri Ramaswamy Memorial (SRM) Dental College & Hospital, Chennai, IND
| | - Santo G Umesh
- Periodontics, Sri Ramaswamy Memorial (SRM) Dental College, Chennai, IND
| | - Sucharitha P
- Periodontics, Sri Ramaswamy Memorial (SRM) Dental College, Chennai, IND
| | - Anupama Tadepalli
- Periodontics & Oral Implantology, Sri Ramaswamy Memorial (SRM) Dental College & Hospital, Chennai, IND
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20
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Elnaggar M, Alharbi ZA, Alanazi AM, Alsaiari SO, Alhemaidani AM, Alanazi SF, Alanazi MM. Assessment of the Perception and Worries of Saudi Healthcare Providers About the Application of Artificial Intelligence in Saudi Health Facilities. Cureus 2023; 15:e42858. [PMID: 37664374 PMCID: PMC10473439 DOI: 10.7759/cureus.42858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/02/2023] [Indexed: 09/05/2023] Open
Abstract
Objective This study is aimed at assessing the perception and worries of Saudi healthcare providers about the application of artificial intelligence (AI) in Saudi healthcare facilities. Methods The study adopted a cross-sectional study involving 1026 Saudi healthcare providers between January 2023 and April 2023. The target population was healthcare providers across Saudi health facilities. Online questionnaires were administered through social media platforms. Data were analyzed using SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY) to obtain important insights. Results The results of this study indicated that more than half (55.2%) of the respondents had good knowledge of AI, with (48.1%) of them being familiar with the application of AI in their specialty. A good proportion of the participants (57.9%) knew at least one term about the difference between machine learning and deep learning. More than half (69.9%) of the participants indicated that they had at one point in time used speech recognition or transcription application in their work. A large section (73.3%) of healthcare providers believed that AI would replace them at their job. A vast majority (84.9%) of the participants agreed that collaboration between medical schools with engineering and computer science faculties could be a game changer to provide a road for incorporating AI into medical curricula. The mean perception of AI in this study was 37.6 (SD=8.41; range 0-241). Age, level of health, health profession, and working experience all significantly impacted the positive perception score (p=0.021; p=0.031; p=0.041; p=0.026). However, there was no significant association between gender, nationality, and Saudi regions with a mean positive perception score. Conclusion There was a positive perception of AI among Saudi healthcare providers. Even though a substantial majority of Saudi healthcare providers were worried that AI would replace their jobs, the study revealed that AI serves as a crucial practitioner's tool rather than a physician's replacement.
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Affiliation(s)
- Marwa Elnaggar
- Department of Community and Family Medicine, College of Medicine, Jouf University, Sakakah, SAU
- Department of Medical Education, College of Medicine, Suez Canal University, Ismailia, EGY
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Shafi I, Fatima A, Afzal H, Díez IDLT, Lipari V, Breñosa J, Ashraf I. A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health. Diagnostics (Basel) 2023; 13:2196. [PMID: 37443594 DOI: 10.3390/diagnostics13132196] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/14/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.
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Affiliation(s)
- Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Anum Fatima
- National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Hammad Afzal
- Military College of Signals (MCS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Vivian Lipari
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Fundación Universitaria Internacional de Colombia, Bogotá 111311, Colombia
| | - Jose Breñosa
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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22
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Artificial intelligence applications in implant dentistry: A systematic review. J Prosthet Dent 2023; 129:293-300. [PMID: 34144789 DOI: 10.1016/j.prosdent.2021.05.008] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 12/21/2022]
Abstract
STATEMENT OF PROBLEM Artificial intelligence (AI) applications are growing in dental implant procedures. The current expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed. PURPOSE The purpose of this systematic review was to assess the performance of AI models in implant dentistry for implant type recognition, implant success prediction by using patient risk factors and ontology criteria, and implant design optimization combining finite element analysis (FEA) calculations and AI models. MATERIAL AND METHODS An electronic systematic review was completed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Peer-reviewed studies that developed AI models for implant type recognition, implant success prediction, and implant design optimization were included. The search strategy included articles published until February 21, 2021. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS Seventeen articles were included: 7 investigations analyzed AI models for implant type recognition, 7 studies included AI prediction models for implant success forecast, and 3 studies evaluated AI models for optimization of implant designs. The AI models developed to recognize implant type by using periapical and panoramic images obtained an overall accuracy outcome ranging from 93.8% to 98%. The models to predict osteointegration success or implant success by using different input data varied among the studies, ranging from 62.4% to 80.5%. Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve the design of dental implants. This improvement includes minimizing the stress at the implant-bone interface by 36.6% compared with the finite element model; optimizing the implant design porosity, length, and diameter to improve the finite element calculations; or accurately determining the elastic modulus of the implant-bone interface. CONCLUSIONS AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are indispensable to the further development and assessment of the clinical performance of AI models for those implant dentistry applications reviewed.
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The U-Net Approaches to Evaluation of Dental Bite-Wing Radiographs: An Artificial Intelligence Study. Diagnostics (Basel) 2023; 13:diagnostics13030453. [PMID: 36766557 PMCID: PMC9914538 DOI: 10.3390/diagnostics13030453] [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: 01/04/2023] [Revised: 01/17/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
Bite-wing radiographs are one of the most used intraoral radiography techniques in dentistry. AI is extremely important in terms of more efficient patient care in the field of dentistry. The aim of this study was to perform a diagnostic evaluation on bite-wing radiographs with an AI model based on CNNs. In this study, 500 bite-wing radiographs in the radiography archive of Eskişehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology were used. The CranioCatch labeling program (CranioCatch, Eskisehir, Turkey) with tooth decays, crowns, pulp, restoration material, and root-filling material for five different diagnoses were made by labeling the segmentation technique. The U-Net architecture was used to develop the AI model. F1 score, sensitivity, and precision results of the study, respectively, caries 0.8818-0.8235-0.9491, crown; 0.9629-0.9285-1, pulp; 0.9631-0.9843-0.9429, with restoration material; and 0.9714-0.9622-0.9807 was obtained as 0.9722-0.9459-1 for the root filling material. This study has shown that an AI model can be used to automatically evaluate bite-wing radiographs and the results are promising. Owing to these automatically prepared charts, physicians in a clinical intense tempo will be able to work more efficiently and quickly.
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Kolarkodi SH, Alotaibi KZ. Artificial Intelligence in Diagnosis of Oral Diseases: A Systematic Review. J Contemp Dent Pract 2023; 24:61-68. [PMID: 37189014 DOI: 10.5005/jp-journals-10024-3465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
AIM To understand the role of Artificial intelligence (AI) in oral radiology and its applications. BACKGROUND Over the last two decades, the field of AI has undergone phenomenal progression and expansion. Artificial intelligence applications have taken up new roles in dentistry like digitized data acquisition and machine learning and diagnostic applications. MATERIALS AND METHODS All research papers outlining the population, intervention, control, and outcomes (PICO) questions were searched for in PubMed, ERIC, Embase, CINAHL, database from the last 10 years on first January 2023. Two authors independently reviewed the titles and abstracts of the selected studies, and any discrepancy between the two review authors was handled by a third reviewer. Two independent investigators evaluated all the included studies for the quality assessment using the modified tool for the quality assessment of diagnostic accuracy studies (QUADAS- 2). REVIEW RESULTS After the removal of duplicates and screening of titles and abstracts, 18 full texts were agreed upon for further evaluation, of which 14 that met the inclusion criteria were included in this review. The application of artificial intelligence models has primarily been reported on osteoporosis diagnosis, classification/segmentation of maxillofacial cysts and/or tumors, and alveolar bone resorption. Overall study quality was deemed to be high for two (14%) studies, moderate for six (43%) studies, and low for another six (43%) studies. CONCLUSION The use of AI for patient diagnosis and clinical decision-making can be accomplished with relative ease, and the technology should be regarded as a reliable modality for potential future applications in oral diagnosis.
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Affiliation(s)
- Shaul Hameed Kolarkodi
- Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Qassim University, Buraydah, Saudi Arabia, Phone: +96 6533653299, e-mail:
| | - Khalid Zabin Alotaibi
- Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Qassim University, Buraydah, Saudi Arabia
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Machine Learning Predictive Model as Clinical Decision Support System in Orthodontic Treatment Planning. Dent J (Basel) 2022; 11:dj11010001. [PMID: 36661538 PMCID: PMC9858447 DOI: 10.3390/dj11010001] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/09/2022] [Accepted: 11/18/2022] [Indexed: 12/24/2022] Open
Abstract
Diagnosis and treatment planning forms the crux of orthodontics, which orthodontists gain with years of expertise. Machine Learning (ML), having the ability to learn by pattern recognition, can gain this expertise in a very short duration, ensuring reduced error, inter-intra clinician variability and good accuracy. Thus, the aim of this study was to construct an ML predictive model to predict a broader outline of the orthodontic diagnosis and treatment plan. The sample consisted of 700 case records of orthodontically treated patients in the past ten years. The data were split into a training and a test set. There were 33 input variables and 11 output variables. Four ML predictive model layers with seven algorithms were created. The test set was used to check the efficacy of the ML-predicted treatment plan and compared with that of the decision made by the expert orthodontists. The model showed an overall average accuracy of 84%, with the Decision Tree, Random Forest and XGB classifier algorithms showing the highest accuracy ranging from 87-93%. Yet in their infancy stages, Machine Learning models could become a valuable Clinical Decision Support System in orthodontic diagnosis and treatment planning in the future.
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Ari T, Sağlam H, Öksüzoğlu H, Kazan O, Bayrakdar İŞ, Duman SB, Çelik Ö, Jagtap R, Futyma-Gąbka K, Różyło-Kalinowska I, Orhan K. Automatic Feature Segmentation in Dental Periapical Radiographs. Diagnostics (Basel) 2022; 12:diagnostics12123081. [PMID: 36553088 PMCID: PMC9777016 DOI: 10.3390/diagnostics12123081] [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: 11/10/2022] [Revised: 11/30/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.
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Affiliation(s)
- Tugba Ari
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26040 Eskişehir, Turkey
| | - Hande Sağlam
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26040 Eskişehir, Turkey
| | - Hasan Öksüzoğlu
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26040 Eskişehir, Turkey
| | - Orhan Kazan
- Health Services Vocational School, Gazi University, 06560 Ankara, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26040 Eskişehir, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer-Aided Diagnosis and Treatment in Health, 26040 Eskişehir, Turkey
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS 39216, USA
| | - Suayip Burak Duman
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, 44000 Malatya, Turkey
| | - Özer Çelik
- Eskisehir Osmangazi University Center of Research and Application for Computer-Aided Diagnosis and Treatment in Health, 26040 Eskişehir, Turkey
- Department of Mathematics-Computer, Faculty of Science, Eskisehir Osmangazi University, 26040 Eskisehir, Turkey
| | - Rohan Jagtap
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS 39216, USA
| | - Karolina Futyma-Gąbka
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-059 Lublin, Poland
| | - Ingrid Różyło-Kalinowska
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-059 Lublin, Poland
- Correspondence: ; Tel.: +48-81-502-1800
| | - Kaan Orhan
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-059 Lublin, Poland
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, 0600 Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), 0600 Ankara, Turkey
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Park EY, Cho H, Kang S, Jeong S, Kim EK. Caries detection with tooth surface segmentation on intraoral photographic images using deep learning. BMC Oral Health 2022; 22:573. [PMID: 36476359 PMCID: PMC9730679 DOI: 10.1186/s12903-022-02589-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm for caries detection through the segmentation of the tooth surface using these images. METHODS In this prospective study, 2348 in-house intraoral photographic images were collected from 445 participants using a professional intraoral camera at a dental clinic in a university medical centre from October 2020 to December 2021. Images were randomly assigned to training (1638), validation (410), and test (300) datasets. For image segmentation of the tooth surface, classification, and localisation of caries, convolutional neural networks (CNN), namely U-Net, ResNet-18, and Faster R-CNN, were applied. RESULTS For the classification algorithm for caries images, the accuracy and area under the receiver operating characteristic curve were improved to 0.813 and 0.837 from 0.758 to 0.731, respectively, through segmentation of the tooth surface using CNN. Localisation algorithm for carious lesions after segmentation of the tooth area also showed improved performance. For example, sensitivity and average precision improved from 0.890 to 0.889 to 0.865 and 0.868, respectively. CONCLUSION The deep learning model with segmentation of the tooth surface is promising for caries detection on photographic images from an intraoral camera. This may be an aided diagnostic method for caries with the advantages of being time and cost-saving.
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Affiliation(s)
- Eun Young Park
- Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Hyeonrae Cho
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea
- School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
| | - Sohee Kang
- Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Sungmoon Jeong
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Eun-Kyong Kim
- Department of Dental Hygiene, College of Science and Technology, Kyungpook National University, 2559 Gyeongsangde-ro, Sangju, Gyeongsangbuk-do, South Korea.
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Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Özcan M, Att W, Krishnamurthy VR. Artificial intelligence applications in restorative dentistry: A systematic review. J Prosthet Dent 2022; 128:867-875. [PMID: 33840515 DOI: 10.1016/j.prosdent.2021.02.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 11/17/2022]
Abstract
STATEMENT OF PROBLEM Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed. PURPOSE The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. MATERIAL AND METHODS An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%. CONCLUSIONS AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry.
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Affiliation(s)
- Marta Revilla-León
- Assistant Professor and Assistant Program Director AEGD Residency, Department of Comprehensive Dentistry, College of Dentistry, Texas A&M University, Dallas, Texas; Affiliate Faculty Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash; Researcher at Revilla Research Center, Madrid, Spain
| | - Miguel Gómez-Polo
- Associate Professor, Department of Conservative Dentistry and Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain.
| | - Shantanu Vyas
- Graduate Research Assistant, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, Dallas, Texas
| | - Abdul Basir Barmak
- Assistant Professor Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, NY
| | - Mutlu Özcan
- Professor and Head, Division of Dental Biomaterials, Clinic for Reconstructive Dentistry, Center for Dental and Oral Medicine, University of Zürich, Zürich, Switzerland
| | - Wael Att
- Professor and Chair, Department of Prosthodontics, Tufts University School of Dental Medicine, Boston, Mass
| | - Vinayak R Krishnamurthy
- Assistant Professor, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas
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Ajami M, Tripathi P, Ling H, Mahdian M. Automated Detection of Cervical Carotid Artery Calcifications in Cone Beam Computed Tomographic Images Using Deep Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12102537. [PMID: 36292226 PMCID: PMC9600983 DOI: 10.3390/diagnostics12102537] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of this study was to determine if a convolutional neural network (CNN) can be trained to automatically detect and localize cervical carotid artery calcifications (CACs) in CBCT. A total of 56 CBCT studies (15,257 axial slices) were utilized to train, validate, and test the deep learning model. The study comprised of two steps: Step 1: Localizing axial slices that are below the C2–C3 disc space. For this step the openly available Inception V3 architecture was trained on the ImageNet dataset of real-world images, and retrained on 40 CBCT studies. Step 2: Detecting CACs in slices from step 1. For this step, two methods were implemented; Method A: Segmentation neural network trained using small patches at random coordinates of the original axial slices; Method B: Segmentation neural network trained using two larger patches at fixed coordinates of the original axial slices with an improved loss function to account for class imbalance. Our approach resulted in 94.2% sensitivity and 96.5% specificity. The mean intersection over union metric for Method A was 76.26% and Method B improved this metric to 82.51%. The proposed CNN model shows the feasibility of deep learning in the detection and localization of CAC in CBCT images.
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Affiliation(s)
- Maryam Ajami
- School of Dental Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Pavani Tripathi
- Department of Computer Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Haibin Ling
- Department of Computer Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, School of Dental Medicine, Stony Brook University, Stony Brook, NY 11794, USA
- Correspondence:
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30
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Bayrakdar IS, Orhan K, Akarsu S, Çelik Ö, Atasoy S, Pekince A, Yasa Y, Bilgir E, Sağlam H, Aslan AF, Odabaş A. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol 2022; 38:468-479. [PMID: 34807344 DOI: 10.1007/s11282-021-00577-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/09/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.
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Affiliation(s)
- Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey.
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Serdar Akarsu
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Samet Atasoy
- Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Adem Pekince
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey
| | - Yasin Yasa
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Hande Sağlam
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
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Bunyarit SS, Nambiar P, Naidu M, Asif MK, Poh RYY. Dental age estimation of Malaysian Indian children and adolescents: applicability of Chaillet and Demirjian's modified method using artificial neural network. Ann Hum Biol 2022; 49:192-199. [PMID: 35997704 DOI: 10.1080/03014460.2022.2105396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
BACKGROUND Recognising the importance of dental age (DA) estimation in forensic investigations, a variety of methods abound in the literature due to population-specific attributes. A reference eight-tooth method developed by Chaillet and Demirjian estimated the DA of children and adolescents. AIM This study aims to investigate the applicability of Chaillet and Demirjian's method among Malaysian Indians aged 5.00-17.99 years. SUBJECTS AND METHODS Dental panoramic tomographs of Malaysian Indians aged 5.00-17.99 years were statistically analysed using paired t-test and artificial neural networks multilayer perceptron (ANN-MLP). RESULTS A total of 1015 dental panoramic tomographs were analysed. Paired t-test analysis against the reference dental maturity scores revealed underestimation of DA in boys of 1.68 years and girls of 2.56 years indicating inaccurate age estimation. A population-specific prediction model with a new set of dental maturity scores was established on Chaillet and Demirjian's scores using ANN-MLP. The new dental maturity scores showed accurate estimation of DA with differences between CA and DA being 12 and 25 days for boys and girls, respectively. Furthermore, a new DA prediction formula was developed using regression analysis following the establishment of new dental scores based on ANN-MLP. CONCLUSION A novel Malaysian Indian-specific prediction model that demonstrated accurate DA estimation was established.
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Affiliation(s)
- Safar Sumit Bunyarit
- Department of Oral & Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia.,Department of Basic Sciences and Oral Biology, Faculty of Dentistry, Universiti Sains Islam Malaysia, Kuala Lumpur, Malaysia
| | - Phrabhakaran Nambiar
- Department of Oral & Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia.,Department of Oral Biology and Biomedical Sciences, Faculty of Dentistry, MAHSA University, Selangor, Malaysia
| | - Murali Naidu
- Department of Anatomy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Muhammad Khan Asif
- Department of Oral & Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia.,Department of Research & Forensic Odontology, Shifa College of Dentistry, Shifa Tameer-e-Millat University, Pakistan
| | - Rozaida Yuen Ying Poh
- Department of Biomedical Science, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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A Survey on the Use of Artificial Intelligence by Clinicians in Dentistry and Oral and Maxillofacial Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58081059. [PMID: 36013526 PMCID: PMC9412897 DOI: 10.3390/medicina58081059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/19/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022]
Abstract
Background: Applications of artificial intelligence (AI) in medicine and dentistry have been on the rise in recent years. In dental radiology, deep learning approaches have improved diagnostics, outperforming clinicians in accuracy and efficiency. This study aimed to provide information on clinicians' knowledge and perceptions regarding AI. Methods: A 21-item questionnaire was used to study the views of dentistry professionals on AI use in clinical practice. Results: In total, 302 questionnaires were answered and assessed. Most of the respondents rated their knowledge of AI as average (37.1%), below average (22.2%) or very poor (23.2%). The participants were largely convinced that AI would improve and bring about uniformity in diagnostics (mean Likert ± standard deviation 3.7 ± 1.27). Among the most serious concerns were the responsibility for machine errors (3.7 ± 1.3), data security or privacy issues (3.5 ± 1.24) and the divestment of healthcare to large technology companies (3.5 ± 1.28). Conclusions: Within the limitations of this study, insights into the acceptance and use of AI in dentistry are revealed for the first time.
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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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Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes.
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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: 24] [Impact Index Per Article: 12.0] [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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Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5032435. [PMID: 35399834 PMCID: PMC8989613 DOI: 10.1155/2022/5032435] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022]
Abstract
Background Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. Objectives The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. Materials and Methods A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. Result This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. Conclusion In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records.
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Görürgöz C, Orhan K, Bayrakdar IS, Çelik Ö, Bilgir E, Odabaş A, Aslan AF, Jagtap R. Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs. Dentomaxillofac Radiol 2022; 51:20210246. [PMID: 34623893 PMCID: PMC8925875 DOI: 10.1259/dmfr.20210246] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. METHODS The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. RESULTS An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. CONCLUSION The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.
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Affiliation(s)
- Cansu Görürgöz
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Bursa Uludağ University, Bursa, Turkey
| | | | | | | | - Elif Bilgir
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics and Computer Science, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Rohan Jagtap
- Division of Oral & Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, Mississippi, USA
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Jeon KJ, Kim YH, Ha EG, Choi HS, Ahn HJ, Lee JR, Hwang D, Han SS. Quantitative analysis of the mouth opening movement of temporomandibular joint disorder patients according to disc position using computer vision: a pilot study. Quant Imaging Med Surg 2022; 12:1909-1918. [PMID: 35284273 PMCID: PMC8899952 DOI: 10.21037/qims-21-629] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/29/2021] [Indexed: 08/27/2023]
Abstract
BACKGROUND Temporomandibular joint disorder (TMD), which is a broad category encompassing disc displacement, is a common condition with an increasing prevalence. This study aimed to develop an automated movement tracing algorithm for mouth opening and closing videos, and to quantitatively analyze the relationship between the results obtained using this developed system and disc position on magnetic resonance imaging (MRI). METHODS Mouth opening and closing videos were obtained with a digital camera from 91 subjects, who underwent MRI. Before video acquisition, an 8.0-mm-diameter circular sticker was attached to the center of the subject's upper and lower lips. The automated mouth opening tracing system based on computer vision was developed in two parts: (I) automated landmark detection of the upper and lower lips in acquired videos, and (II) graphical presentation of the tracing results for detected landmarks and an automatically calculated graph height (mouth opening length) and width (sideways values). The graph paths were divided into three types: straight, sideways-skewed, and limited-straight line graphs. All traced results were evaluated according to disc position groups determined using MRI. Graph height and width were compared between groups using analysis of variance (SPSS version 25.0; IBM Corp., Armonk, NY, USA). RESULTS Subjects with a normal disc position predominantly (85.72%) showed straight line graphs. The other two types (sideways-skewed or limited-straight line graphs) were found in 85.0% and 89.47% in the anterior disc displacement with reduction group and in the anterior disc displacement without reduction group, respectively, reflecting a statistically significant correlation (χ2=38.113, P<0.001). A statistically significant difference in graph height was found between the normal group and the anterior disc displacement without reduction group, 44.90±9.61 and 35.78±10.24 mm, respectively (P<0.05). CONCLUSIONS The developed mouth opening tracing system was reliable. It presented objective and quantitative information about different trajectories from those associated with a normal disc position in mouth opening and closing movements. This system will be helpful to clinicians when it is difficult to obtain information through MRI.
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Affiliation(s)
- Kug Jin Jeon
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Young Hyun Kim
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Eun-Gyu Ha
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Han Seung Choi
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Hyung-Joon Ahn
- Department of Orofacial Pain and Oral Medicine, Dental Hospital, Yonsei University College of Dentistry, Seoul, Korea
| | - Jeong Ryong Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Dosik Hwang
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
- Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
- Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
- Department of Computer Science, Yonsei University, Seoul, Korea
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Kaya E, Gunec HG, Aydin KC, Urkmez ES, Duranay R, Ates HF. A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs. Imaging Sci Dent 2022; 52:275-281. [PMID: 36238699 PMCID: PMC9530294 DOI: 10.5624/isd.20220050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/19/2022] [Accepted: 06/01/2022] [Indexed: 12/01/2022] Open
Abstract
Purpose The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort.
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Affiliation(s)
- Emine Kaya
- Department of Pediatric Dentistry, Faculty of Dentistry, Istanbul Okan University, Istanbul, Turkey
| | - Huseyin Gurkan Gunec
- Department of Endodontics, Faculty of Dentistry, Atlas University, Istanbul, Turkey
| | - Kader Cesur Aydin
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Istanbul Medipol University, Istanbul, Turkey
| | | | - Recep Duranay
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Atlas University, Istanbul, Turkey
| | - Hasan Fehmi Ates
- Department of Computer Engineering, School of Engineering and Natural Sciences, Istanbul Medipol University, Istanbul, Turkey
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do Nascimento Gerhardt M, Shujaat S, Jacobs R. AIM in Dentistry. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol 2022; 51:20210197. [PMID: 34233515 PMCID: PMC8693331 DOI: 10.1259/dmfr.20210197] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.
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Affiliation(s)
| | - Chiaki Doi
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Nobuhiro Yoda
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Eha Renwi Astuti
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo no 47, Surabaya, Indonesia
| | - Keiichi Sasaki
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
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Lingam A, Koppolu P, Akhter F, Afroz M, Tabassum N, Arshed M, Khan T, ElHaddad S. Future trends of artificial intelligence in dentistry. JOURNAL OF NATURE AND SCIENCE OF MEDICINE 2022. [DOI: 10.4103/jnsm.jnsm_2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Başaran M, Çelik Ö, Bayrakdar IS, Bilgir E, Orhan K, Odabaş A, Aslan AF, Jagtap R. Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system. Oral Radiol 2021; 38:363-369. [PMID: 34611840 DOI: 10.1007/s11282-021-00572-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 09/06/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVES The goal of this study was to develop and evaluate the performance of a new deep-learning (DL) artificial intelligence (AI) model for diagnostic charting in panoramic radiography. METHODS One thousand eighty-four anonymous dental panoramic radiographs were labeled by two dento-maxillofacial radiologists for ten different dental situations: crown, pontic, root-canal treated tooth, implant, implant-supported crown, impacted tooth, residual root, filling, caries, and dental calculus. AI Model CranioCatch, developed in Eskişehir, Turkey and based on a deep CNN method, was proposed to be evaluated. A Faster R-CNN Inception v2 (COCO) model implemented with the TensorFlow library was used for model development. The assessment of AI model performance was evaluated with sensitivity, precision, and F1 scores. RESULTS When the performance of the proposed AI model for detecting dental conditions in panoramic radiographs was evaluated, the best sensitivity values were obtained from the crown, implant, and impacted tooth as 0.9674, 0.9615, and 0.9658, respectively. The worst sensitivity values were obtained from the pontic, caries, and dental calculus, as 0.7738, 0.3026, and 0.0934, respectively. The best precision values were obtained from pontic, implant, implant-supported crown as 0.8783, 0.9259, and 0.8947, respectively. The worst precision values were obtained from residual root, caries, and dental calculus, as 0.6764, 0.5096, and 0.1923, respectively. The most successful F1 Scores were obtained from the implant, crown, and implant-supported crown, as 0.9433, 0.9122, and 0.8947, respectively. CONCLUSION The proposed AI model has promising results at detecting dental conditions in panoramic radiographs, except for caries and dental calculus. Thanks to the improvement of AI models in all areas of dental radiology, we predict that they will help physicians in panoramic diagnosis and treatment planning, as well as digital-based student education, especially during the pandemic period.
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Affiliation(s)
- Melike Başaran
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kütahya Health Science University, Kütahya, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskişehir, Turkey.,Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskişehir, Turkey
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskişehir, Turkey. .,Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskişehir, Turkey.
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey.,Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskişehir, Turkey
| | - Rohan Jagtap
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA
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44
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Duong DL, Kabir MH, Kuo RF. Automated caries detection with smartphone color photography using machine learning. Health Informatics J 2021; 27:14604582211007530. [PMID: 33863251 DOI: 10.1177/14604582211007530] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Untreated caries is significant problem that affected billion people over the world. Therefore, the appropriate method and accuracy of caries detection in clinical decision-making in dental practices as well as in oral epidemiology or caries research, are required urgently. The aim of this study was to introduce a computational algorithm that can automate recognize carious lesions on tooth occlusal surfaces in smartphone images according to International Caries Detection and Assessment System (ICDAS). From a group of extracted teeth, 620 unrestored molars/premolars were photographed using smartphone. The obtained images were evaluated for caries diagnosis with the ICDAS II codes, and were labeled into three classes: "No Surface Change" (NSC); "Visually Non-Cavitated" (VNC); "Cavitated" (C). Then, a two steps detection scheme using Support Vector Machine (SVM) has been proposed: "C versus (VNC + NSC)" classification, and "VNC versus NSC" classification. The accuracy, sensitivity, and specificity of best model were 92.37%, 88.1%, and 96.6% for "C versus (VNC + NSC)," whereas they were 83.33%, 82.2%, and 66.7% for "VNC versus NSC." Although the proposed SVM system required further improvement and verification, with the data only imaged from the smartphone, it performed an auspicious potential for clinical diagnostics with reasonable accuracy and minimal cost.
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Affiliation(s)
| | | | - Rong Fu Kuo
- Department of Biomedical Engineering, National Cheng Kung University.,Medical Device Innovation Center, National Cheng Kung University
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45
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Wenzel A. Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems. Dentomaxillofac Radiol 2021; 50:20210010. [PMID: 33661697 PMCID: PMC8231685 DOI: 10.1259/dmfr.20210010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/03/2021] [Accepted: 02/16/2021] [Indexed: 01/17/2023] Open
Abstract
Radiographic imaging for the diagnosis of caries lesions has been a supplement to clinical examination for approximately a century. Various methods, and particularly X-ray receptors, have been developed over the years, and computer systems have focused on aiding the dentist in the detection of lesions and in estimating lesion depth. The present historical review has sampled accuracy ex vivo studies and clinical studies on radiographic caries diagnosis that have compared two or more receptors for capturing the image. The epochs of film radiography, xeroradiography, digital intraoral radiography, panoramic radiography and other extraoral methods, TACT analysis, cone-beam CT and artificial intelligence systems aiding in decision-making are reviewed. The author of this review (43 years in academia) has been involved in caries research and contributed to the literature in all the mentioned epochs.
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Affiliation(s)
- Ann Wenzel
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark
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46
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Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, Alam MK. Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9751564. [PMID: 34258283 PMCID: PMC8245240 DOI: 10.1155/2021/9751564] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/30/2021] [Accepted: 06/05/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry. MATERIALS AND METHODS Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted. RESULTS The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics. CONCLUSION The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.
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Affiliation(s)
- Naseer Ahmed
- Prosthodontics Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kota Bharu, Kelantan, Malaysia
- Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
| | - Maria Shakoor Abbasi
- Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
| | - Filza Zuberi
- Undergraduate Student Bachelor of Dental Surgery, Dow Dental College, Dow University of Health Sciences, Karachi 74200, Pakistan
| | - Warisha Qamar
- Research Intern, Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
| | - Mohamad Syahrizal Bin Halim
- Conservative Dentistry Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kota Bharu, Kelantan, Malaysia
| | - Afsheen Maqsood
- Department of Oral Pathology, Bahria University Medical and Dental College, Karachi 75530, Pakistan
| | - Mohammad Khursheed Alam
- Department of Preventive Dentistry, College of Dentistry, Jouf University, Sakaka, Al Jouf, 72345, Saudi Arabia
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47
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Kurt Bayrakdar S, Orhan K, Bayrakdar IS, Bilgir E, Ezhov M, Gusarev M, Shumilov E. A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging 2021; 21:86. [PMID: 34011314 PMCID: PMC8132372 DOI: 10.1186/s12880-021-00618-z] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 05/05/2021] [Indexed: 12/20/2022] Open
Abstract
Background The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. Conclusions Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.
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Affiliation(s)
- Sevda Kurt Bayrakdar
- Department of Periodontology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, 06500, Ankara, Turkey. .,Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara, Turkey.
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.,Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
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48
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Yasa Y, Çelik Ö, Bayrakdar IS, Pekince A, Orhan K, Akarsu S, Atasoy S, Bilgir E, Odabaş A, Aslan AF. An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs. Acta Odontol Scand 2021; 79:275-281. [PMID: 33176533 DOI: 10.1080/00016357.2020.1840624] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVES Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method. METHODS The study included 1125 bite-wing radiographs of patients who attended the Faculty of Dentistry of Ordu University from 2018 to 2019. A faster R-CNN an advanced object identification method was used to identify the teeth. The confusion matrix was used as a metric and to evaluate the success of the model. RESULTS The deep CNN system (CranioCatch, Eskisehir, Turkey) was used to detect and number teeth in bitewing radiographs. Of 715 teeth in 109 bite-wing images, 697 were correctly numbered in the test data set. The F1 score, precision and sensitivity were 0.9515, 0.9293 and 0.9748, respectively. CONCLUSIONS A CNN approach for the analysis of bitewing images shows promise for detecting and numbering teeth. This method can save dentists time by automatically preparing dental charts.
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Affiliation(s)
- Yasin Yasa
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Adem Pekince
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Serdar Akarsu
- Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Samet Atasoy
- Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
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49
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Müller A, Mertens SM, Göstemeyer G, Krois J, Schwendicke F. Barriers and Enablers for Artificial Intelligence in Dental Diagnostics: A Qualitative Study. J Clin Med 2021; 10:1612. [PMID: 33920189 PMCID: PMC8069285 DOI: 10.3390/jcm10081612] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 12/22/2022] Open
Abstract
The present study aimed to identify barriers and enablers for the implementation of artificial intelligence (AI) in dental, specifically radiographic, diagnostics. Semi-structured phone interviews with dentists and patients were conducted between the end of May and the end of June 2020 (convenience/snowball sampling). A questionnaire developed along the Theoretical Domains Framework (TDF) and the Capabilities, Opportunities and Motivations influencing Behaviors model (COM-B) was used to guide interviews. Mayring's content analysis was employed to point out barriers and enablers. We identified 36 barriers, conflicting themes or enablers, covering nine of the fourteen domains of the TDF and all three determinants of behavior (COM). Both stakeholders emphasized chances and hopes for AI. A range of enablers for implementing AI in dental diagnostics were identified (e.g., the chance for higher diagnostic accuracy, a reduced workload, more comprehensive reporting and better patient-provider communication). Barriers related to reliance on AI and responsibility for medical decisions, as well as the explainability of AI and the related option to de-bug AI applications, emerged. Decision-makers and industry may want to consider these aspects to foster implementation of AI in dentistry.
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Affiliation(s)
- Anne Müller
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (A.M.); (J.K.)
| | - Sarah Marie Mertens
- Department of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (S.M.M.); (G.G.)
| | - Gerd Göstemeyer
- Department of Operative and Preventive Dentistry, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (S.M.M.); (G.G.)
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (A.M.); (J.K.)
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany; (A.M.); (J.K.)
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50
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Kılıc MC, Bayrakdar IS, Çelik Ö, Bilgir E, Orhan K, Aydın OB, Kaplan FA, Sağlam H, Odabaş A, Aslan AF, Yılmaz AB. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol 2021; 50:20200172. [PMID: 33661699 DOI: 10.1259/dmfr.20200172] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. METHODS AND MATERIALS An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. RESULTS The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. CONCLUSION Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.
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Affiliation(s)
- Münevver Coruh Kılıc
- Department of Paediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum, Turkey
| | - Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
| | - Ozan Barıs Aydın
- Department of Paediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum, Turkey
| | - Fatma Akkoca Kaplan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Hande Sağlam
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Alper Odabaş
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
| | - Ahmet Berhan Yılmaz
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, Turkey, Turkey
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