Dental Panoramik Radyografide Yapay Zeka Sistemi Kullanılarak Alveoler Kemik Kaybının Belirlenmesi
Öz
Anahtar Kelimeler
References
- 1. Dentino A, Lee S, Mailhot J, Hefti AF. Principles of periodontology. Periodontology 2000 2013; 61:16-53.
- 2. Mol A. Imaging methods in periodontology. Periodontology 2000 2004; 34:34-48.
- 3. Tonetti MS, Jepsen S, Jin L, Otomo‐Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action. Journal of clinical periodontology 2017; 44:456-462.
- 4. Clerehugh V, Tugnait A. Diagnosis and management of periodontal diseases in children and adolescents. Periodontology 2000 2001; 26:146-168.
- 5. Scarfe WC, Azevedo B, Pinheiro LR, Priaminiarti M, Sales MA. The emerging role of maxillofacial radiology in the diagnosis and management of patients with complex periodontitis. Periodontology 2000 2017; 74:116-139.
- 6. Rushton V, Horner K. The use of panoramic radiology in dental practice. Journal of dentistry 1996; 24:185-201.
- 7. Kaimenyi J, Ashley F. Assessment of bone loss in periodontitis from panoramic radiographs. Journal of clinical periodontology 1988; 15:170-174.
- 8. Chartrand G, Cheng PM, Vorontsov Eet al. Deep learning: a primer for radiologists. Radiographics 2017; 37:2113-2131.
Details
Primary Language
English
Subjects
Health Care Administration
Journal Section
Research Article
Authors
Sevda Kurt
0000-0002-3711-6520
Türkiye
Özer Çelik
Türkiye
Kaan Orhan
0000-0001-6768-0176
Türkiye
Elif Bilgir
0000-0001-9521-4682
Türkiye
Alper Odabas
0000-0002-4361-3056
Türkiye
Ahmet Faruk Aslan
0000-0003-1583-6508
Türkiye
Publication Date
December 31, 2020
Submission Date
August 4, 2020
Acceptance Date
October 6, 2020
Published in Issue
Year 1970 Volume: 23 Number: 4
Cited By
C.E. Credit. Artificial Intelligence Applications for the Radiographic Detection of Periodontal Disease: A Scoping Review
Journal of the California Dental Association
https://doi.org/10.1080/19424396.2023.2206301Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study
Diagnostics
https://doi.org/10.3390/diagnostics13101800Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss—A scoping review
Australian Endodontic Journal
https://doi.org/10.1111/aej.12775Deep learning in periodontology and oral implantology: A scoping review
Journal of Periodontal Research
https://doi.org/10.1111/jre.13037The Impetus of Artificial Intelligence on Periodontal Diagnosis: A Brief Synopsis
Cureus
https://doi.org/10.7759/cureus.43583Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review
Imaging Science in Dentistry
https://doi.org/10.5624/isd.20230092Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging
International Journal of Imaging Systems and Technology
https://doi.org/10.1002/ima.22973Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks
Diagnostics
https://doi.org/10.3390/diagnostics13233562A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level
Journal of Dentistry
https://doi.org/10.1016/j.jdent.2022.104211Artificial Intelligence in Periodontology: A Scoping Review
Dentistry Journal
https://doi.org/10.3390/dj11020043Accuracy of convolutional neural network in the diagnosis of alveolar bone loss due to periodontal disease: A systematic review and meta-analysis
Journal of Datta Meghe Institute of Medical Sciences University
https://doi.org/10.4103/jdmimsu.jdmimsu_281_22Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis
BMC Oral Health
https://doi.org/10.1186/s12903-023-03751-zDetection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study
BMC Oral Health
https://doi.org/10.1186/s12903-024-03896-5Classification of Periapical and Bitewing Radiographs as Periodontally Healthy or Diseased by Deep Learning Algorithms
Cureus
https://doi.org/10.7759/cureus.60550Periodontitis diagnosis: A review of current and future trends in artificial intelligence
Technology and Health Care
https://doi.org/10.3233/THC-241169Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities
Diagnostics
https://doi.org/10.3390/diagnostics14202336Artificial intelligence-powered dentistry: Probing the potential, challenges, and ethicality of artificial intelligence in dentistry
DIGITAL HEALTH
https://doi.org/10.1177/20552076241291345Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study
BMC Oral Health
https://doi.org/10.1186/s12903-024-05268-5Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis
Dentomaxillofacial Radiology
https://doi.org/10.1093/dmfr/twae070Artificial intelligence-powered innovations in periodontal diagnosis: a new era in dental healthcare
Frontiers in Medical Technology
https://doi.org/10.3389/fmedt.2024.1469852Advanced AI-assisted panoramic radiograph analysis for periodontal prognostication and alveolar bone loss detection
Frontiers in Dental Medicine
https://doi.org/10.3389/fdmed.2024.1509361Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss
Clinical Oral Investigations
https://doi.org/10.1007/s00784-025-06283-8Emerging Applications of Digital Technologies for Periodontal Screening, Diagnosis and Prognosis in the Dental Setting
Journal of Clinical Periodontology
https://doi.org/10.1111/jcpe.14156Artificial Intelligence Tools in Dentistry: A Systematic Review on Their Application and Outcomes
Cureus
https://doi.org/10.7759/cureus.85062Automated Detection of Periodontal Bone Loss in Two-Dimensional (2D) Radiographs Using Artificial Intelligence: A Systematic Review
Dentistry Journal
https://doi.org/10.3390/dj13090413Tooth-level detection and mapping of dental pathologies on panoramic radiographs using YOLOv11 and RT-DETR
MethodsX
https://doi.org/10.1016/j.mex.2025.103696Validity and Reliability of Responses to Periodontology Questions by 4 Different Artificial Intelligence Chatbots as Public Information Sources
Cumhuriyet Dental Journal
https://doi.org/10.7126/cumudj.1673333AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Scientific Reports
https://doi.org/10.1038/s41598-026-38061-1Diagnostic accuracy of artificial intelligence‐based deep learning models in detecting furcation involvement: A systematic review and meta‐analysis
Journal of Periodontology
https://doi.org/10.1002/jper.70055Artificial Intelligence in Periodontology: A Systematic Review
Journal of Periodontal Research
https://doi.org/10.1111/jre.70107