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Soheili F, Delfan N, Masoudifar N, Ebrahimni S, Moshiri B, Glogauer M, Ghafar-Zadeh E. Toward Digital Periodontal Health: Recent Advances and Future Perspectives. Bioengineering (Basel) 2024; 11:937. [PMID: 39329678 PMCID: PMC11428937 DOI: 10.3390/bioengineering11090937] [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: 08/08/2024] [Revised: 08/24/2024] [Accepted: 09/12/2024] [Indexed: 09/28/2024] Open
Abstract
Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.
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Affiliation(s)
- Fatemeh Soheili
- Biologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
- Department of Biology, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
| | - Niloufar Delfan
- Biologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran P9FQ+M8X, Kargar, Iran
| | - Negin Masoudifar
- Department of Internal Medicine, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Shahin Ebrahimni
- Biologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
| | - Behzad Moshiri
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran P9FQ+M8X, Kargar, Iran
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Michael Glogauer
- Faculty of Dentistry, University of Toronto, Toronto, ON M5G 1G6, Canada
| | - Ebrahim Ghafar-Zadeh
- Biologically Inspired Sensors and Actuators Laboratory (BIOSA), Lassonde School of Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
- Department of Biology, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
- Department of Electrical Engineering and Computer Science, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
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Erturk M, Öziç MÜ, Tassoker M. Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01218-3. [PMID: 39147888 DOI: 10.1007/s10278-024-01218-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 07/16/2024] [Accepted: 07/29/2024] [Indexed: 08/17/2024]
Abstract
Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment requirements. This study aims to automatically stage periodontal bone loss using a deep learning approach using bite-wing images. A total of 1752 bite-wing images were used for the study. Radiological examinations were classified into 4 groups. Healthy (normal), no bone loss; stage I (mild destruction), bone loss in the coronal third (< 15%); stage II (moderate destruction), bone loss is in the coronal third and from 15 to 33% (15-33%); stage III-IV (severe destruction), bone loss extending from the middle third to the apical third with furcation destruction (> 33%). All images were converted to 512 × 400 dimensions using bilinear interpolation. The data was divided into 80% training validation and 20% testing. The classification module of the YOLOv8 deep learning model was used for the artificial intelligence-based classification of the images. Based on four class results, it was trained using fivefold cross-validation after transfer learning and fine tuning. After the training, 20% of test data, which the system had never seen, were analyzed using the artificial intelligence weights obtained in each cross-validation. Training and test results were calculated with average accuracy, precision, recall, and F1-score performance metrics. Test images were analyzed with Eigen-CAM explainability heat maps. In the classification of bite-wing images as healthy, mild destruction, moderate destruction, and severe destruction, training performance results were 86.100% accuracy, 84.790% precision, 82.350% recall, and 84.411% F1-score, and test performance results were 83.446% accuracy, 81.742% precision, 80.883% recall, and 81.090% F1-score. The deep learning model gave successful results in staging periodontal bone loss in bite-wing images. Classification scores were relatively high for normal (no bone loss) and severe bone loss in bite-wing images, as they are more clearly visible than mild and moderate damage.
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Affiliation(s)
- Mediha Erturk
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey
| | - Muhammet Üsame Öziç
- Faculty of Technology Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey.
| | - Melek Tassoker
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey
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Qutieshat A, Al Rusheidi A, Al Ghammari S, Alarabi A, Salem A, Zelihic M. Comparative analysis of diagnostic accuracy in endodontic assessments: dental students vs. artificial intelligence. Diagnosis (Berl) 2024; 11:259-265. [PMID: 38696271 DOI: 10.1515/dx-2024-0034] [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: 02/16/2024] [Accepted: 04/22/2024] [Indexed: 05/04/2024]
Abstract
OBJECTIVES This study evaluates the comparative diagnostic accuracy of dental students and artificial intelligence (AI), specifically a modified ChatGPT 4, in endodontic assessments related to pulpal and apical conditions. The findings are intended to offer insights into the potential role of AI in augmenting dental education. METHODS Involving 109 dental students divided into junior (54) and senior (55) groups, the study compared their diagnostic accuracy against ChatGPT's across seven clinical scenarios. Juniors had the American Association of Endodontists (AEE) terminology assistance, while seniors relied on prior knowledge. Accuracy was measured against a gold standard by experienced endodontists, using statistical analysis including Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests. RESULTS ChatGPT achieved significantly higher accuracy (99.0 %) compared to seniors (79.7 %) and juniors (77.0 %). Median accuracy was 100.0 % for ChatGPT, 85.7 % for seniors, and 82.1 % for juniors. Statistical tests indicated significant differences between ChatGPT and both student groups (p<0.001), with no notable difference between the student cohorts. CONCLUSIONS The study reveals AI's capability to outperform dental students in diagnostic accuracy regarding endodontic assessments. This underscores AIs potential as a reference tool that students could utilize to enhance their understanding and diagnostic skills. Nevertheless, the potential for overreliance on AI, which may affect the development of critical analytical and decision-making abilities, necessitates a balanced integration of AI with human expertise and clinical judgement in dental education. Future research is essential to navigate the ethical and legal frameworks for incorporating AI tools such as ChatGPT into dental education and clinical practices effectively.
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Affiliation(s)
- Abubaker Qutieshat
- Adult Restorative Dentistry, 442177 Oman Dental College , Muscat, Oman
- Restorative Dentistry, Dundee Dental Hospital and School, University of Dundee, Dundee, UK
| | | | | | | | - Abdurahman Salem
- Dental Technology, 1796 School of Health & Society, University of Bolton , Greater Manchester, UK
| | - Maja Zelihic
- Forbes School of Business and Technology, 191123 University of Arizona Global Campus , Chandler, AZ, USA
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Xu S, Peng H, Yang L, Zhong W, Gao X, Song J. An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1800-1811. [PMID: 38393620 PMCID: PMC11300848 DOI: 10.1007/s10278-024-01045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for clinical intervention. This study aimed to evaluate six deep convolutional neural networks (CNNs) for performing OIERR grading on tooth slices to construct an automatic grading system for OIERR. A total of 2146 tooth slices of different OIERR grades were collected and preprocessed. Six pre-trained CNNs (EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, and MobileNet-V3) were trained and validated on the pre-processed images based on four different cross-validation methods. The performances of the CNNs on a test set were evaluated and compared with those of orthodontists. The gradient-weighted class activation mapping (Grad-CAM) technique was used to explore the area of maximum impact on the model decisions in the tooth slices. The six CNN models performed remarkably well in OIERR grading, with a mean accuracy of 0.92, surpassing that of the orthodontists (mean accuracy of 0.82). EfficientNet-B4 trained with fivefold cross-validation emerged as the final OIERR grading system, with a high accuracy of 0.94. Grad-CAM revealed that the apical region had the greatest effect on the OIERR grading system. The six CNNs demonstrated excellent OIERR grading and outperformed orthodontists. The proposed OIERR grading system holds potential as a reliable diagnostic support for orthodontists in clinical practice.
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Affiliation(s)
- Shuxi Xu
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Houli Peng
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Lanxin Yang
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Wenjie Zhong
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Xiang Gao
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China.
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China.
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China.
| | - Jinlin Song
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China.
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China.
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China.
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Alrashed S, Dutra V, Chu TMG, Yang CC, Lin WS. Influence of exposure protocol, voxel size, and artifact removal algorithm on the trueness of segmentation utilizing an artificial-intelligence-based system. J Prosthodont 2024; 33:574-583. [PMID: 38305665 DOI: 10.1111/jopr.13827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 01/09/2024] [Indexed: 02/03/2024] Open
Abstract
PURPOSE To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)-based system. MATERIALS AND METHODS Eleven dry human mandibles were scanned using a cone beam computed tomography (CBCT) scanner under differing exposure protocols (standard and ultra-low), voxel sizes (0.15 mm, 0.3 mm, and 0.45 mm), and with or without artifact removal algorithm. The resulting datasets were segmented using an AI-based system, exported as 3D models, and compared to reference files derived from a white-light laboratory scanner. Deviation measurement was performed using a computer-aided design (CAD) program and recorded as root mean square (RMS). The RMS values were used as a representation of the trueness of the AI-segmented 3D models. A 4-way ANOVA was used to assess the impact of voxel size, exposure protocol, artifact removal algorithm, and location on RMS values (α = 0.05). RESULTS Significant effects were found with voxel size (p < 0.001) and location (p < 0.001), but not with exposure protocol (p = 0.259) or artifact removal algorithm (p = 0.752). Standard exposure groups had significantly lower RMS values than the ultra-low exposure groups in the mandible body with 0.3 mm (p = 0.014) or 0.45 mm (p < 0.001) voxel sizes, the symphysis with a 0.45 mm voxel size (p = 0.011), and the whole mandible with a 0.45 mm voxel size (p = 0.001). Exposure protocol did not affect RMS values at teeth and alveolar bone (p = 0.544), mandible angles (p = 0.380), condyles (p = 0.114), and coronoids (p = 0.806) locations. CONCLUSION This study informs optimal exposure protocol and voxel size choices in CBCT imaging for true AI-based automatic segmentation with minimal radiation. The artifact removal algorithm did not influence the trueness of AI segmentation. When using an ultra-low exposure protocol to minimize patient radiation exposure in AI segmentations, a voxel size of 0.15 mm is recommended, while a voxel size of 0.45 mm should be avoided.
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Affiliation(s)
- Safa Alrashed
- Oral Biology PhD program in the College of Dentistry, Division of Restorative and Prosthetic Dentistry, The Ohio State University, Columbus, Ohio, USA
| | - Vinicius Dutra
- Department of Oral Pathology, Medicine, and Radiology, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Tien-Min G Chu
- Department of Biomedical Sciences and Comprehensive Care, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Chao-Chieh Yang
- Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
- Advanced Education Program in Prosthodontics, Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Wei-Shao Lin
- Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
- Advanced Education Program in Prosthodontics, Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
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Semerci ZM, Yardımcı S. Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making. Diagnostics (Basel) 2024; 14:1260. [PMID: 38928675 PMCID: PMC11202919 DOI: 10.3390/diagnostics14121260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 06/06/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.
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Affiliation(s)
- Zeliha Merve Semerci
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Akdeniz University, Antalya 07070, Turkey
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Jacobs R, Fontenele RC, Lahoud P, Shujaat S, Bornstein MM. Radiographic diagnosis of periodontal diseases - Current evidence versus innovations. Periodontol 2000 2024; 95:51-69. [PMID: 38831570 DOI: 10.1111/prd.12580] [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: 02/07/2024] [Revised: 04/23/2024] [Accepted: 05/16/2024] [Indexed: 06/05/2024]
Abstract
Accurate diagnosis of periodontal and peri-implant diseases relies significantly on radiographic examination, especially for assessing alveolar bone levels, bone defect morphology, and bone quality. This narrative review aimed to comprehensively outline the current state-of-the-art in radiographic diagnosis of alveolar bone diseases, covering both two-dimensional (2D) and three-dimensional (3D) modalities. Additionally, this review explores recent technological advances in periodontal imaging diagnosis, focusing on their potential integration into clinical practice. Clinical probing and intraoral radiography, while crucial, encounter limitations in effectively assessing complex periodontal bone defects. Recognizing these challenges, 3D imaging modalities, such as cone beam computed tomography (CBCT), have been explored for a more comprehensive understanding of periodontal structures. The significance of the radiographic assessment approach is evidenced by its ability to offer an objective and standardized means of evaluating hard tissues, reducing variability associated with manual clinical measurements and contributing to a more precise diagnosis of periodontal health. However, clinicians should be aware of challenges related to CBCT imaging assessment, including beam-hardening artifacts generated by the high-density materials present in the field of view, which might affect image quality. Integration of digital technologies, such as artificial intelligence-based tools in intraoral radiography software, the enhances the diagnostic process. The overarching recommendation is a judicious combination of CBCT and digital intraoral radiography for enhanced periodontal bone assessment. Therefore, it is crucial for clinicians to weigh the benefits against the risks associated with higher radiation exposure on a case-by-case basis, prioritizing patient safety and treatment outcomes.
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Affiliation(s)
- Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Rocharles Cavalcante Fontenele
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Pierre Lahoud
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Leuven, Belgium
| | - Sohaib Shujaat
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Michael M Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
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Sáiz-Manzanares MC, Solórzano Mulas A, Escolar-Llamazares MC, Alcantud Marín F, Rodríguez-Arribas S, Velasco-Saiz R. Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis. CHILDREN (BASEL, SWITZERLAND) 2024; 11:381. [PMID: 38671598 PMCID: PMC11048911 DOI: 10.3390/children11040381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/28/2024]
Abstract
Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0-6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.
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Affiliation(s)
- María Consuelo Sáiz-Manzanares
- DATAHES Research Group, Consolidated Research Unit Nº. 348, Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
| | | | - María Camino Escolar-Llamazares
- DATAHES Research Group, Consolidated Research Unit Nº. 348, Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
| | - Francisco Alcantud Marín
- Department of Developmental and Educational Psychology, Universitat de València, 46010 València, Spain;
| | - Sandra Rodríguez-Arribas
- BEST-AI Research Group, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain;
| | - Rut Velasco-Saiz
- Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain;
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Cheung K, Cheung W, Liu Y, Ye H, Lv L, Zhou Y. Establishment of a 3D esthetic analysis workflow on 3D virtual patient and preliminary evaluation. BMC Oral Health 2024; 24:328. [PMID: 38475773 DOI: 10.1186/s12903-024-04085-0] [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: 08/31/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND In esthetic dentistry, a thorough esthetic analysis holds significant role in both diagnosing diseases and designing treatment plans. This study established a 3D esthetic analysis workflow based on 3D facial and dental models, and aimed to provide an imperative foundation for the artificial intelligent 3D analysis in future esthetic dentistry. METHODS The established 3D esthetic analysis workflow includes the following steps: 1) key point detection, 2) coordinate system redetermination and 3) esthetic parameter calculation. The accuracy and reproducibility of this established workflow were evaluated by a self-controlled experiment (n = 15) in which 2D esthetic analysis and direct measurement were taken as control. Measurement differences between 3D and 2D analysis were evaluated with paired t-tests. RESULTS 3D esthetic analysis demonstrated high consistency and reliability (0.973 < ICC < 1.000). Compared with 2D measurements, the results from 3D esthetic measurements were closer to direct measurements regarding tooth-related esthetic parameters (P<0.05). CONCLUSIONS The 3D esthetic analysis workflow established for 3D virtual patients demonstrated a high level of consistency and reliability, better than 2D measurements in the precision of tooth-related parameter analysis. These findings indicate a highly promising outlook for achieving an objective, precise, and efficient esthetic analysis in the future, which is expected to result in a more streamlined and user-friendly digital design process. This study was registered with the Ethics Committee of Peking University School of Stomatology in September 2021 with the registration number PKUSSIRB-202168136.
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Affiliation(s)
- Kwantong Cheung
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Waisze Cheung
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Yunsong Liu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Hongqiang Ye
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Longwei Lv
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China.
| | - Yongsheng Zhou
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China.
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10
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Yu H, Ye X, Hong W, Shi R, Ding Y, Liu C. A cascading learning method with SegFormer for radiographic measurement of periodontal bone loss. BMC Oral Health 2024; 24:325. [PMID: 38468273 PMCID: PMC10929133 DOI: 10.1186/s12903-024-04079-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/27/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVE Marginal alveolar bone loss is one of the key features of periodontitis and can be observed via panoramic radiographs. This study aimed to establish a cascading learning method with deep learning (DL) for precise radiographic bone loss (RBL) measurements at specific tooth positions. MATERIALS AND METHODS Through the design of two tasks for tooth position recognition and tooth semantic segmentation using the SegFormer model, specific tooth's crown, intrabony portion, and suprabony portion of the roots were obtained. The RBL was subsequently measured by length through these three areas using the principal component analysis (PCA) principal axis. RESULTS The average intersection over union (IoU) for the tooth position recognition task was 0.8906, with an F1-score of 0.9338. The average IoU for the tooth semantic segmentation task was 0.8465, with an F1-score of 0.9138. When the two tasks were combined, the average IoU was 0.7889, with an F1-score of 0.8674. The correlation coefficient between the RBL prediction results based on the PCA principal axis and the clinicians' measurements exceeded 0.85. Compared to those of the other two methods, the average precision of the predicted RBL was 0.7722, the average sensitivity was 0.7416, and the average F1-score was 0.7444. CONCLUSIONS The method for predicting RBL using DL and PCA produced promising results, offering rapid and reliable auxiliary information for future periodontal disease diagnosis. CLINICAL RELEVANCE Precise RBL measurements are important for periodontal diagnosis. The proposed RBL-SF can measure RBL at specific tooth positions and assign the bone loss stage. The ability of the RBL-SF to measure RBL at specific tooth positions can guide clinicians to a certain extent in the accurate diagnosis of periodontitis.
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Affiliation(s)
- Hanwen Yu
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Xin Ye
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Wanjing Hong
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Rui Shi
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Yi Ding
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Chengcheng Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
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11
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Kurt-Bayrakdar S, Bayrakdar İŞ, Yavuz MB, Sali N, Çelik Ö, Köse O, Uzun Saylan BC, Kuleli B, Jagtap R, Orhan K. Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study. BMC Oral Health 2024; 24:155. [PMID: 38297288 PMCID: PMC10832206 DOI: 10.1186/s12903-024-03896-5] [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: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns. METHODS A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. RESULTS The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). CONCLUSIONS AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.
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Affiliation(s)
- Sevda Kurt-Bayrakdar
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey.
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA.
| | - İbrahim Şevki Bayrakdar
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Muhammet Burak Yavuz
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey
| | - Nichal Sali
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey
| | - Özer Çelik
- Faculty of Science, Department of Mathematics and Computer Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Oğuz Köse
- Faculty of Dentistry, Department of Periodontology, Recep Tayyip Erdogan University, Rize, Turkey
| | | | - Batuhan Kuleli
- Faculty of Dentistry, Department of Orthodontics, Eskisehir Osmangazi University, 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, USA
| | - Kaan Orhan
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Ankara University, Ankara, Turkey
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12
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Guler Ayyildiz B, Karakis R, Terzioglu B, Ozdemir D. Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages. Dentomaxillofac Radiol 2024; 53:32-42. [PMID: 38214940 PMCID: PMC11003609 DOI: 10.1093/dmfr/twad003] [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: 07/05/2023] [Revised: 10/20/2023] [Accepted: 11/06/2023] [Indexed: 01/13/2024] Open
Abstract
OBJECTIVES The objective of this study is to assess the accuracy of computer-assisted periodontal classification bone loss staging using deep learning (DL) methods on panoramic radiographs and to compare the performance of various models and layers. METHODS Panoramic radiographs were diagnosed and classified into 3 groups, namely "healthy," "Stage1/2," and "Stage3/4," and stored in separate folders. The feature extraction stage involved transferring and retraining the feature extraction layers and weights from 3 models, namely ResNet50, DenseNet121, and InceptionV3, which were proposed for classifying the ImageNet dataset, to 3 DL models designed for classifying periodontal bone loss. The features obtained from global average pooling (GAP), global max pooling (GMP), or flatten layers (FL) of convolutional neural network (CNN) models were used as input to the 8 different machine learning (ML) models. In addition, the features obtained from the GAP, GMP, or FL of the DL models were reduced using the minimum redundancy maximum relevance (mRMR) method and then classified again with 8 ML models. RESULTS A total of 2533 panoramic radiographs, including 721 in the healthy group, 842 in the Stage1/2 group, and 970 in the Stage3/4 group, were included in the dataset. The average performance values of DenseNet121 + GAP-based and DenseNet121 + GAP + mRMR-based ML techniques on 10 subdatasets and ML models developed using 2 feature selection techniques outperformed CNN models. CONCLUSIONS The new DenseNet121 + GAP + mRMR-based support vector machine model developed in this study achieved higher performance in periodontal bone loss classification compared to other models in the literature by detecting effective features from raw images without the need for manual selection.
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Affiliation(s)
- Berceste Guler Ayyildiz
- Faculty of Dentistry, Department of Periodontology, Kutahya Health Sciences University, Kutahya, 43100, Turkey
| | - Rukiye Karakis
- Faculty of Technology, Department of Software Engineering, Sivas Cumhuriyet University, Sivas, 58140, Turkey
| | - Busra Terzioglu
- Faculty of Dentistry, Department of Periodontology, Kutahya Health Sciences University, Kutahya, 43100, Turkey
- Tavsanlõ Vocational School, Oral Health Department, Kutahya Health Sciences University, Kütahya, 43410, Turkey
| | - Durmus Ozdemir
- Faculty of Engineering, Department of Computer Engineering, Kutahya Dumlupinar University, Kutahya, 43020, Turkey
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13
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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:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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14
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Li X, Zhao D, Xie J, Wen H, Liu C, Li Y, Li W, Wang S. Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis. BMC Oral Health 2023; 23:1017. [PMID: 38114946 PMCID: PMC10729340 DOI: 10.1186/s12903-023-03751-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/08/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach. METHODS A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed. RESULTS Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94). CONCLUSIONS The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.
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Affiliation(s)
- Xin Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Dan Zhao
- Department of Implant Dentistry, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Jinxuan Xie
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Hao Wen
- City University of Hong Kong, Hong Kong SAR, China
| | - Chunhua Liu
- City University of Hong Kong, Hong Kong SAR, China
| | - Yajie Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Wenbin Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songlin Wang
- Salivary Gland Disease Center and Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction, Beijing Laboratory of Oral Health and Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China.
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15
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Dujic H, Meyer O, Hoss P, Wölfle UC, Wülk A, Meusburger T, Meier L, Gruhn V, Hesenius M, Hickel R, Kühnisch J. Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks. Diagnostics (Basel) 2023; 13:3562. [PMID: 38066803 PMCID: PMC10706472 DOI: 10.3390/diagnostics13233562] [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: 10/26/2023] [Revised: 11/18/2023] [Accepted: 11/27/2023] [Indexed: 07/25/2024] Open
Abstract
Several artificial intelligence-based models have been presented for the detection of periodontal bone loss (PBL), mostly using convolutional neural networks, which are the state of the art in deep learning. Given the emerging breakthrough of transformer networks in computer vision, we aimed to evaluate various models for automatized PBL detection. An image data set of 21,819 anonymized periapical radiographs from the upper/lower and anterior/posterior regions was assessed by calibrated dentists according to PBL. Five vision transformer networks (ViT-base/ViT-large from Google, BEiT-base/BEiT-large from Microsoft, DeiT-base from Facebook/Meta) were utilized and evaluated. Accuracy (ACC), sensitivity (SE), specificity (SP), positive/negative predictive value (PPV/NPV) and area under the ROC curve (AUC) were statistically determined. The overall diagnostic ACC and AUC values ranged from 83.4 to 85.2% and 0.899 to 0.918 for all evaluated transformer networks, respectively. Differences in diagnostic performance were evident for lower (ACC 94.1-96.7%; AUC 0.944-0.970) and upper anterior (86.7-90.2%; 0.948-0.958) and lower (85.6-87.2%; 0.913-0.937) and upper posterior teeth (78.1-81.0%; 0.851-0.875). In this study, only minor differences among the tested networks were detected for PBL detection. To increase the diagnostic performance and to support the clinical use of such networks, further optimisations with larger and manually annotated image data sets are needed.
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Affiliation(s)
- Helena Dujic
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
| | - Ole Meyer
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Patrick Hoss
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
| | - Uta Christine Wölfle
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
| | - Annika Wülk
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
| | - Theresa Meusburger
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
| | - Leon Meier
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
| | - Volker Gruhn
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Marc Hesenius
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Reinhard Hickel
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.)
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16
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Hoss P, Meyer O, Wölfle UC, Wülk A, Meusburger T, Meier L, Hickel R, Gruhn V, Hesenius M, Kühnisch J, Dujic H. Detection of Periodontal Bone Loss on Periapical Radiographs-A Diagnostic Study Using Different Convolutional Neural Networks. J Clin Med 2023; 12:7189. [PMID: 38002799 PMCID: PMC10672399 DOI: 10.3390/jcm12227189] [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: 11/03/2023] [Revised: 11/14/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023] Open
Abstract
Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.
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Affiliation(s)
- Patrick Hoss
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Ole Meyer
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Uta Christine Wölfle
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Annika Wülk
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Theresa Meusburger
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Leon Meier
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Reinhard Hickel
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Volker Gruhn
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Marc Hesenius
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Helena Dujic
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
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Radha RC, Raghavendra BS, Subhash BV, Rajan J, Narasimhadhan AV. Machine learning techniques for periodontitis and dental caries detection: A narrative review. Int J Med Inform 2023; 178:105170. [PMID: 37595373 DOI: 10.1016/j.ijmedinf.2023.105170] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 08/20/2023]
Abstract
OBJECTIVES In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. METHODS An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. RESULTS The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. CONCLUSION While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction.
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Affiliation(s)
- R C Radha
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India.
| | - B S Raghavendra
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - B V Subhash
- Department of Oral Medicine and Radiology, DAPM R V Dental College, Bengaluru, India
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - A V Narasimhadhan
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India
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Singhal I, Kaur G, Neefs D, Pathak A. A Literature Review of the Future of Oral Medicine and Radiology, Oral Pathology, and Oral Surgery in the Hands of Technology. Cureus 2023; 15:e45804. [PMID: 37876387 PMCID: PMC10591112 DOI: 10.7759/cureus.45804] [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] [Accepted: 09/22/2023] [Indexed: 10/26/2023] Open
Abstract
In the realm of dentistry, a myriad of technological advancements, including teledentistry, virtual reality (VR), artificial intelligence (AI), and three-dimensional printing, have been extensively embraced and rigorously evaluated, consistently demonstrating their remarkable effectiveness. These innovations have ushered in a transformative era in dentistry, impacting every facet of the field. They encompass activities ranging from the diagnosis and exploration of oral health conditions to the formulation of treatment plans, execution of surgical procedures, fabrication of prosthetics, and even assistance in patient distraction, prognosis, and disease prevention. Despite the significant strides already taken, the relentless pursuit of new horizons fueled by human curiosity remains unabated. The future landscape of dentistry holds the promise of sweeping changes, notably characterized by enhanced accessibility to dental care and reduced treatment durations. In this comprehensive review article, we delve into the pivotal roles played by AI, VR, augmented reality, mixed reality, and extended reality within the realm of dentistry, with a particular emphasis on their applications in oral medicine, oral radiology, oral surgery, and oral pathology. These technologies represent just a fraction of the technological arsenal currently harnessed in the field of dentistry. A thorough comprehension of their advantages and limitations is imperative for informed decision-making in their utilization.
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Affiliation(s)
- Ishita Singhal
- Oral Pathology and Microbiology and Forensic Odontology, Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram, IND
| | - Geetpriya Kaur
- Oral Pathology and Microbiology, Paradise Diagnostics, New Delhi, IND
| | - Dirk Neefs
- Dentistry, Dierick Dental Care, Antwerp, BEL
| | - Aparna Pathak
- Oral Pathology, Paradise Diagnostics, New Delhi, IND
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19
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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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Uzun Saylan BC, Baydar O, Yeşilova E, Kurt Bayrakdar S, Bilgir E, Bayrakdar İŞ, Çelik Ö, Orhan K. Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study. Diagnostics (Basel) 2023; 13:diagnostics13101800. [PMID: 37238284 DOI: 10.3390/diagnostics13101800] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/13/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
The assessment of alveolar bone loss, a crucial element of the periodontium, plays a vital role in the diagnosis of periodontitis and the prognosis of the disease. In dentistry, artificial intelligence (AI) applications have demonstrated practical and efficient diagnostic capabilities, leveraging machine learning and cognitive problem-solving functions that mimic human abilities. This study aims to evaluate the effectiveness of AI models in identifying alveolar bone loss as present or absent across different regions. To achieve this goal, alveolar bone loss models were generated using the PyTorch-based YOLO-v5 model implemented via CranioCatch software, detecting periodontal bone loss areas and labeling them using the segmentation method on 685 panoramic radiographs. Besides general evaluation, models were grouped according to subregions (incisors, canines, premolars, and molars) to provide a targeted evaluation. Our findings reveal that the lowest sensitivity and F1 score values were associated with total alveolar bone loss, while the highest values were observed in the maxillary incisor region. It shows that artificial intelligence has a high potential in analytical studies evaluating periodontal bone loss situations. Considering the limited amount of data, it is predicted that this success will increase with the provision of machine learning by using a more comprehensive data set in further studies.
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Affiliation(s)
- Bilge Cansu Uzun Saylan
- Department of Periodontology, Faculty of Dentistry, Dokuz Eylul University, İzmir 35220, Turkey
| | - Oğuzhan Baydar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ege University, İzmir 35040, Turkey
| | - Esra Yeşilova
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Sevda Kurt Bayrakdar
- Department of Periodontology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Elif Bilgir
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26480, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06830, Turkey
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21
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Widyaningrum R, Candradewi I, Aji NRAS, Aulianisa R. Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis. Imaging Sci Dent 2022; 52:383-391. [PMID: 36605859 PMCID: PMC9807794 DOI: 10.5624/isd.20220105] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/03/2022] [Accepted: 09/09/2022] [Indexed: 12/28/2022] Open
Abstract
Purpose Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.
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Affiliation(s)
- Rini Widyaningrum
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Ika Candradewi
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | | | - Rona Aulianisa
- Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
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