Treffer: Artificial intelligence model for application in dental traumatology.
Title:
Artificial intelligence model for application in dental traumatology.
Authors:
Bani-Hani T; Division of Pediatric Dentistry, Preventive Dentistry Department, Faculty of Dentistry, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan. tgbanihani@just.edu.jo., Wedyan M; Department of Computer Sciences, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan., Al-Fodeh R; Department of Prosthodontics, Faculty of Dentistry, Jordan University of Science and Technology, Irbid, 22110, Jordan., Shuqeir R; Pediatric Dentistry, Jordan University of Science and Technology, Irbid, 22110, Jordan., Al Jundi S; Division of Pediatric Dentistry, Preventive Dentistry Department, Faculty of Dentistry, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan., Tewari N; Pediatric and Preventive Dentistry, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, 110029, India.
Source:
European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry [Eur Arch Paediatr Dent] 2025 Dec; Vol. 26 (6), pp. 1117-1124. Date of Electronic Publication: 2025 May 31.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: England NLM ID: 101277157 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1996-9805 (Electronic) Linking ISSN: 18186300 NLM ISO Abbreviation: Eur Arch Paediatr Dent Subsets: MEDLINE
Imprint Name(s):
Publication: 2013- : London : Springer
Original Publication: Leeds, England : European Academy of Paediatric Dentistry, [2006]-
Original Publication: Leeds, England : European Academy of Paediatric Dentistry, [2006]-
MeSH Terms:
References:
Ahmed WM, Azhari AA, Fawaz KA, et al. Artificial intelligence in the detection and classification of dental caries. J Prosthet Dent. 2023;23:478. https://doi.org/10.1016/j.prosdent.2023.07.013 . (PMID: 10.1016/j.prosdent.2023.07.013)
Almalki YE, Din AI, Ramzan M, et al. Deep learning models for classification of dental diseases using orthopantomography X-ray OPG images. Sensors. 2022;28(19):7370. https://doi.org/10.3390/s22197370 . (PMID: 10.3390/s22197370)
Alzaid N, Ghulam O, Albani M, et al. Revolutionizing dental care: a comprehensive review of artificial intelligence applications among various dental specialties. Cureus. 2023;15(10): e47033. https://doi.org/10.7759/cureus.47033 . (PMID: 10.7759/cureus.470333796539710642940)
Bani-Hani TG, Olegário IC, O’Connell AC. The cost of dental trauma management: a one-year prospective study in children. Dent Traumatol. 2020;36(5):526–32. https://doi.org/10.1111/edt.12561 . (PMID: 10.1111/edt.1256132330346)
Bani-Hani T, Al-Fodeh R, Bataineh M, Tabnjh A. Are we adequately managing the oral health needs of children: a survey of the experience of general dentists and self-assessed confidence in pediatric dentistry. J Clin Pediatr Dent. 2024;48(4):176–84. https://doi.org/10.22514/jocpd.2024.091 . (PMID: 10.22514/jocpd.2024.09139087228)
Bourguignon C, Cohenca N, Lauridsen E, et al. International Association of Dental Traumatology guidelines for the management of traumatic dental injuries: 1. Fractures and luxations. Dent Traumatol. 2020;36(4):314–30. https://doi.org/10.1111/edt.12578 . (PMID: 10.1111/edt.1257832475015)
Chang HJ, Lee SJ, Yong TH, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020;10(1):7531. https://doi.org/10.1038/s41598-020-64509-z . (PMID: 10.1038/s41598-020-64509-z323720497200807)
Chaudhary S, Singh H, Gharti A, Adhikari B. Evaluation of clinical and radiographic findings among patients with traumatic dental injuries seeking delayed treatment. Int J Dent. 2021;2021:9549508. https://doi.org/10.1155/2021/9549508 . (PMID: 10.1155/2021/9549508344714108405333)
Chlap P, Min H, Vandenberg N, et al. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545–63. https://doi.org/10.1111/1754-9485.13261 . (PMID: 10.1111/1754-9485.1326134145766)
Chung SW, Han SS, Lee JW, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89(4):468–73. https://doi.org/10.1080/17453674.2018.1453714 . (PMID: 10.1080/17453674.2018.1453714295777916066766)
Cully M, Cully J, Nietert PJ, Titus MO. Physician confidence in dental trauma treatment and the introduction of a dental trauma decision-making pathway for the pediatric emergency department. Pediatr Emerg Care. 2019;35(11):745–8. https://doi.org/10.1097/PEC.0000000000001479 . (PMID: 10.1097/PEC.0000000000001479296983387153725)
Eckle K, Schmidt-Hieber J. A comparison of deep networks with ReLU activation function and linear spline-type methods. Neural Netw. 2019;110:232–42. https://doi.org/10.1016/j.neunet.2018.11.005 . (PMID: 10.1016/j.neunet.2018.11.00530616095)
Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505–15. https://doi.org/10.1148/rg.2017160130 . (PMID: 10.1148/rg.201716013028212054)
Fouad AF, Abbott PV, Tsilingaridis G, et al. International association of dental traumatology guidelines for the management of traumatic dental injuries: 2. Avulsion of permanent teeth. Dent Traumatol. 2020;36(4):331–42. https://doi.org/10.1111/edt.1257 . (PMID: 10.1111/edt.125732460393)
Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337–43. https://doi.org/10.1007/s11282-019-00409-x . (PMID: 10.1007/s11282-019-00409-x31535278)
Fushiki T. Estimation of prediction error by using K-fold cross-validation. Stat Comput. 2011;21:137–46. (PMID: 10.1007/s11222-009-9153-8)
Glendor UL. Epidemiology of traumatic dental injuries–a 12 year review of the literature. Dent Traumatol. 2008;24(6):603–11. https://doi.org/10.1111/j.1600-9657.2008.00696.x . (PMID: 10.1111/j.1600-9657.2008.00696.x19021651)
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10. https://doi.org/10.1001/jama.2016.17216 . (PMID: 10.1001/jama.2016.1721627898976)
Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73(5):439–45. https://doi.org/10.1016/j.crad.2017.11.01520 .
Kumar T, Brennan R, Mileo A, Bendechache M. Image data augmentation approaches: A comprehensive survey and future directions. IEEE Access. 2024 . arXiv preprint arXiv:2301.0283.
Kuru HE, Aşık A, Demir DM. Can artificial intelligence language models effectively address dental trauma questions? Dent Traumatol. 2025. https://doi.org/10.1111/edt.13063 . (PMID: 10.1111/edt.130634017027012424120)
Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Front Public Health. 2023;7(11):1273253. https://doi.org/10.3389/fpubh.2023.1273253 . (PMID: 10.3389/fpubh.2023.1273253)
Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep convolutional neural networks in medical image analysis: a review. Information. 2025;16(3):195. https://doi.org/10.3390/info16030195 . (PMID: 10.3390/info16030195)
Mohammad N, Muad AM, Ahmad R, Yusof MYPM. Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging. BMC Med Imaging. 2022;22(1):66. https://doi.org/10.1186/s12880-022-00794-6 . (PMID: 10.1186/s12880-022-00794-6353957378991580)
Mustuloğlu Ş, Deniz BP. Evaluation of Chatbots in the emergency management of avulsion injuries. Dent Traumatol. 2025. https://doi.org/10.1111/edt.13041 . (PMID: 10.1111/edt.130413986537712260122)
Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial intelligence in adult and pediatric dentistry: a narrative review. Bioengineering (Basel). 2024;11(5):431. https://doi.org/10.3390/bioengineering11050431 . (PMID: 10.3390/bioengineering1105043138790300)
Ozden I, Gokyar M, Ozden ME, Sazak OH. Assessment of artificial intelligence applications in responding to dental trauma. Dent Traumatol. 2024;40(6):722–9. https://doi.org/10.1111/edt.1296 . (PMID: 10.1111/edt.129638742754)
Petti S, Glendor U, Andersson L. World traumatic dental injury prevalence and incidence, a meta-analysis-one billion living people have had traumatic dental injuries. Dent Traumatol. 2018;34(2):71–86. (PMID: 10.1111/edt.1238929455471)
Sevinç E. An empowered AdaBoost algorithm implementation: a COVID- 19 dataset study. Comput Ind Eng. 2022;165(107):912.
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48. https://doi.org/10.1146/annurev-bioeng-071516-044442 . (PMID: 10.1146/annurev-bioeng-071516-044442283017345479722)
Subramanian AK, Chen Y, Almalki A, Sivamurthy G, Kafle D. Cephalometric analysis in orthodontics using artificial intelligence-a comprehensive review. Biomed Res Int. 2022;2022:1880113. https://doi.org/10.1155/2022/1880113 . (PMID: 10.1155/2022/1880113357574869225851)
Urban R, Haluzová S, Strunga M, et al. AI-assisted CBCT data management in modern dental practice: benefits, limitations and innovations. Electronics. 2023;12(7):1710. https://doi.org/10.3390/electronics1207171 . (PMID: 10.3390/electronics1207171)
Vishwanathaiah S, Fageeh HN, Khanagar SB, Maganur PC. Artificial intelligence its uses and application in pediatric dentistry: a review. Biomedicines. 2023;11(3):788. https://doi.org/10.3390/biomedicines11030788 . (PMID: 10.3390/biomedicines110307883697976710044793)
Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611–29. https://doi.org/10.1007/s13244-018-0639-9 . (PMID: 10.1007/s13244-018-0639-9299349206108980)
Yeng T, O’Sullivan AJ, Shulruf B. Medical doctors’ knowledge of dental trauma management: a review. Dent Traumatol. 2020;36(2):100–7. https://doi.org/10.1111/edt.1251 . (PMID: 10.1111/edt.125131609070)
Almalki YE, Din AI, Ramzan M, et al. Deep learning models for classification of dental diseases using orthopantomography X-ray OPG images. Sensors. 2022;28(19):7370. https://doi.org/10.3390/s22197370 . (PMID: 10.3390/s22197370)
Alzaid N, Ghulam O, Albani M, et al. Revolutionizing dental care: a comprehensive review of artificial intelligence applications among various dental specialties. Cureus. 2023;15(10): e47033. https://doi.org/10.7759/cureus.47033 . (PMID: 10.7759/cureus.470333796539710642940)
Bani-Hani TG, Olegário IC, O’Connell AC. The cost of dental trauma management: a one-year prospective study in children. Dent Traumatol. 2020;36(5):526–32. https://doi.org/10.1111/edt.12561 . (PMID: 10.1111/edt.1256132330346)
Bani-Hani T, Al-Fodeh R, Bataineh M, Tabnjh A. Are we adequately managing the oral health needs of children: a survey of the experience of general dentists and self-assessed confidence in pediatric dentistry. J Clin Pediatr Dent. 2024;48(4):176–84. https://doi.org/10.22514/jocpd.2024.091 . (PMID: 10.22514/jocpd.2024.09139087228)
Bourguignon C, Cohenca N, Lauridsen E, et al. International Association of Dental Traumatology guidelines for the management of traumatic dental injuries: 1. Fractures and luxations. Dent Traumatol. 2020;36(4):314–30. https://doi.org/10.1111/edt.12578 . (PMID: 10.1111/edt.1257832475015)
Chang HJ, Lee SJ, Yong TH, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020;10(1):7531. https://doi.org/10.1038/s41598-020-64509-z . (PMID: 10.1038/s41598-020-64509-z323720497200807)
Chaudhary S, Singh H, Gharti A, Adhikari B. Evaluation of clinical and radiographic findings among patients with traumatic dental injuries seeking delayed treatment. Int J Dent. 2021;2021:9549508. https://doi.org/10.1155/2021/9549508 . (PMID: 10.1155/2021/9549508344714108405333)
Chlap P, Min H, Vandenberg N, et al. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545–63. https://doi.org/10.1111/1754-9485.13261 . (PMID: 10.1111/1754-9485.1326134145766)
Chung SW, Han SS, Lee JW, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89(4):468–73. https://doi.org/10.1080/17453674.2018.1453714 . (PMID: 10.1080/17453674.2018.1453714295777916066766)
Cully M, Cully J, Nietert PJ, Titus MO. Physician confidence in dental trauma treatment and the introduction of a dental trauma decision-making pathway for the pediatric emergency department. Pediatr Emerg Care. 2019;35(11):745–8. https://doi.org/10.1097/PEC.0000000000001479 . (PMID: 10.1097/PEC.0000000000001479296983387153725)
Eckle K, Schmidt-Hieber J. A comparison of deep networks with ReLU activation function and linear spline-type methods. Neural Netw. 2019;110:232–42. https://doi.org/10.1016/j.neunet.2018.11.005 . (PMID: 10.1016/j.neunet.2018.11.00530616095)
Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505–15. https://doi.org/10.1148/rg.2017160130 . (PMID: 10.1148/rg.201716013028212054)
Fouad AF, Abbott PV, Tsilingaridis G, et al. International association of dental traumatology guidelines for the management of traumatic dental injuries: 2. Avulsion of permanent teeth. Dent Traumatol. 2020;36(4):331–42. https://doi.org/10.1111/edt.1257 . (PMID: 10.1111/edt.125732460393)
Fukuda M, Inamoto K, Shibata N, et al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020;36(4):337–43. https://doi.org/10.1007/s11282-019-00409-x . (PMID: 10.1007/s11282-019-00409-x31535278)
Fushiki T. Estimation of prediction error by using K-fold cross-validation. Stat Comput. 2011;21:137–46. (PMID: 10.1007/s11222-009-9153-8)
Glendor UL. Epidemiology of traumatic dental injuries–a 12 year review of the literature. Dent Traumatol. 2008;24(6):603–11. https://doi.org/10.1111/j.1600-9657.2008.00696.x . (PMID: 10.1111/j.1600-9657.2008.00696.x19021651)
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10. https://doi.org/10.1001/jama.2016.17216 . (PMID: 10.1001/jama.2016.1721627898976)
Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73(5):439–45. https://doi.org/10.1016/j.crad.2017.11.01520 .
Kumar T, Brennan R, Mileo A, Bendechache M. Image data augmentation approaches: A comprehensive survey and future directions. IEEE Access. 2024 . arXiv preprint arXiv:2301.0283.
Kuru HE, Aşık A, Demir DM. Can artificial intelligence language models effectively address dental trauma questions? Dent Traumatol. 2025. https://doi.org/10.1111/edt.13063 . (PMID: 10.1111/edt.130634017027012424120)
Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Front Public Health. 2023;7(11):1273253. https://doi.org/10.3389/fpubh.2023.1273253 . (PMID: 10.3389/fpubh.2023.1273253)
Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep convolutional neural networks in medical image analysis: a review. Information. 2025;16(3):195. https://doi.org/10.3390/info16030195 . (PMID: 10.3390/info16030195)
Mohammad N, Muad AM, Ahmad R, Yusof MYPM. Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging. BMC Med Imaging. 2022;22(1):66. https://doi.org/10.1186/s12880-022-00794-6 . (PMID: 10.1186/s12880-022-00794-6353957378991580)
Mustuloğlu Ş, Deniz BP. Evaluation of Chatbots in the emergency management of avulsion injuries. Dent Traumatol. 2025. https://doi.org/10.1111/edt.13041 . (PMID: 10.1111/edt.130413986537712260122)
Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial intelligence in adult and pediatric dentistry: a narrative review. Bioengineering (Basel). 2024;11(5):431. https://doi.org/10.3390/bioengineering11050431 . (PMID: 10.3390/bioengineering1105043138790300)
Ozden I, Gokyar M, Ozden ME, Sazak OH. Assessment of artificial intelligence applications in responding to dental trauma. Dent Traumatol. 2024;40(6):722–9. https://doi.org/10.1111/edt.1296 . (PMID: 10.1111/edt.129638742754)
Petti S, Glendor U, Andersson L. World traumatic dental injury prevalence and incidence, a meta-analysis-one billion living people have had traumatic dental injuries. Dent Traumatol. 2018;34(2):71–86. (PMID: 10.1111/edt.1238929455471)
Sevinç E. An empowered AdaBoost algorithm implementation: a COVID- 19 dataset study. Comput Ind Eng. 2022;165(107):912.
Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48. https://doi.org/10.1146/annurev-bioeng-071516-044442 . (PMID: 10.1146/annurev-bioeng-071516-044442283017345479722)
Subramanian AK, Chen Y, Almalki A, Sivamurthy G, Kafle D. Cephalometric analysis in orthodontics using artificial intelligence-a comprehensive review. Biomed Res Int. 2022;2022:1880113. https://doi.org/10.1155/2022/1880113 . (PMID: 10.1155/2022/1880113357574869225851)
Urban R, Haluzová S, Strunga M, et al. AI-assisted CBCT data management in modern dental practice: benefits, limitations and innovations. Electronics. 2023;12(7):1710. https://doi.org/10.3390/electronics1207171 . (PMID: 10.3390/electronics1207171)
Vishwanathaiah S, Fageeh HN, Khanagar SB, Maganur PC. Artificial intelligence its uses and application in pediatric dentistry: a review. Biomedicines. 2023;11(3):788. https://doi.org/10.3390/biomedicines11030788 . (PMID: 10.3390/biomedicines110307883697976710044793)
Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018;9(4):611–29. https://doi.org/10.1007/s13244-018-0639-9 . (PMID: 10.1007/s13244-018-0639-9299349206108980)
Yeng T, O’Sullivan AJ, Shulruf B. Medical doctors’ knowledge of dental trauma management: a review. Dent Traumatol. 2020;36(2):100–7. https://doi.org/10.1111/edt.1251 . (PMID: 10.1111/edt.125131609070)
Contributed Indexing:
Keywords: Artificial intelligence; CNNs; Convolutional neural networks; Deep learning; Dental fractures
Entry Date(s):
Date Created: 20250531 Date Completed: 20251121 Latest Revision: 20251121
Update Code:
20251121
DOI:
10.1007/s40368-025-01063-0
PMID:
40448917
Database:
MEDLINE
Weitere Informationen
Background: In recent years, healthcare systems have witnessed a tremendous advancement in diagnostic tools and technologies. The advent of artificial intelligence (AI) has enabled a paradigm shift in the practice of health sciences particularly in medicine. In the dental field, AI has been scarcely used in the various disciplines with no application in dental traumatology. This study proposes a deep-learning, convolutional neural networks (CNN)-based model for detection and classification of dental fractures.
Methods: Plain periapical radiographs of injured teeth were retrieved from patients' records and annotated by two dentists trained in dental traumatology. The teeth were categorised into four groups: uncomplicated crown fractures, complicated crown fractures, crown-root fractures and root fractures. Data augmentation was done to enhance the power of the current dataset. Images were divided into training (80%) and test (20%) datasets. Python programming language was used to implement the CNN-based classification model. Cross validation was applied.
Results: A total of 72 plain periapical radiographs of 108 fractured teeth were collected. The model achieved high accuracy in differentiating uncomplicated crown fractures from complicated ones (96.0%), from crown-root fractures (99.1%) and from root fractures (98.7%). Furthermore, the complicated injuries were distinguished from crown-root fractures and from root fractures with accuracy levels at 96.3% and 97.2% respectively. The model's overall accuracy in recognising the four classes was 78.7%.
Conclusion: The proposed model showed excellent performance in the classification of dental fractures. The application of AI in paediatric dentistry, particularly in the field of dental trauma, is innovative and highly relevant to current trends in healthcare technology. Expansion of the current model to a larger dataset that includes the various types of injuries is recommended in future research. Such models can be a great asset for the less-experienced dentists in making accurate diagnosis and timely decisions. Future models employing panoramic radiographs could also help the medical practitioners at emergency services.
(© 2025. The Author(s), under exclusive licence to European Academy of Paediatric Dentistry.)
Declarations. Conflict of interest: All authors declare that there is no conflict of interest.