Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
권호 표지
DOI QR코드

DOI QR Code

Efficiency and accuracy of artificial intelligence in the radiographic detection of periodontal bone loss: A systematic review

  • Asmhan Tariq (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Fatmah Bin Nakhi (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Fatema Salah (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Gabass Eltayeb (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Ghada Jassem Abdulla (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Noor Najim (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Salma Ahmed Khedr (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Sara Elkerdasy (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Natheer Al-Rawi (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Sausan Alkawas (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Marwan Mohammed (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah) ;
  • Shishir Ram Shetty (Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah)
  • Received : 2023.04.26
  • Accepted : 2023.06.23
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: Artificial intelligence (AI) is poised to play a major role in medical diagnostics. Periodontal disease is one of the most common oral diseases. The early diagnosis of periodontal disease is essential for effective treatment and a favorable prognosis. This study aimed to assess the effectiveness of AI in diagnosing periodontal bone loss through radiographic analysis. Materials and Methods: A literature search involving 5 databases (PubMed, ScienceDirect, Scopus, Health and Medical Collection, Dentistry and Oral Sciences) was carried out. A specific combination of keywords was used to obtain the articles. The PRISMA guidelines were used to filter eligible articles. The study design, sample size, type of AI software, and the results of each eligible study were analyzed. The CASP diagnostic study checklist was used to evaluate the evidence strength score. Results: Seven articles were eligible for review according to the PRISMA guidelines. Out of the 7 eligible studies, 4 had strong CASP evidence strength scores (7-8/9). The remaining studies had intermediate CASP evidence strength scores (3.5-6.5/9). The highest area under the curve among the reported studies was 94%, the highest F1 score was 91%, and the highest specificity and sensitivity were 98.1% and 94%, respectively. Conclusion: AI-based detection of periodontal bone loss using radiographs is an efficient method. However, more clinical studies need to be conducted before this method is introduced into routine dental practice.

Keywords

References

  1. Highfield J. Diagnosis and classification of periodontal disease. Aust Dent J 2009; 54 Suppl 1: S11-26. https://doi.org/10.1111/j.1834-7819.2009.01140.x
  2. Bourgeois D, Inquimbert C, Ottolenghi L, Carrouel F. Periodontal pathogens as risk factors of cardiovascular diseases, diabetes, rheumatoid arthritis, cancer, and chronic obstructive pulmonary disease - is there cause for consideration? Microorganisms 2019; 7: 424.
  3. Nazir M, Al-Ansari A, Al-Khalifa K, Alhareky M, Gaffar B, Almas K. Global prevalence of periodontal disease and lack of its surveillance. ScientificWorldJournal 2020; 2020: 2146160.
  4. Ramenzoni L, Lehner M, Kaufmann M, Wiedemeier D, Attin T, Schmidlin PR. Oral diagnostic methods for the detection of periodontal disease. Diagnostics(Basel) 2021; 11: 571.
  5. Al-Rawi N, Sultan A, Rajai B, Shuaeeb H, Alnajjar M, Alketbi M, et al. The effectiveness of artificial intelligence in detection of oral cancer. Int Dent J 2022; 72: 436-47. https://doi.org/10.1016/j.identj.2022.03.001
  6. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: a survey. Heliyon 2018; 4: e00938.
  7. Shetty S, Reddy S, Shetty R, Halkai, R, Shetty S, Halkai, K. Machine learning in maxillofacial radiology: a review. J Datta Meghe Inst Med Sci Univ 2021; 16: 794-6. https://doi.org/10.4103/jdmimsu.jdmimsu_303_20
  8. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71.
  9. casp-uk.net [Internet]. Oxford: Critical appraisal skills programme; 2023 [cited 2023 March 4]. Available from https://www.casp-uk.net.
  10. Kim J, Lee HS, Song IS, Jung KH. DeNTNet: deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep 2019; 9: 17615.
  11. Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep 2020; 10: 7531.
  12. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep 2019; 9: 8495.
  13. Kurt Bayrakdar S, Celiko, Bayrakdar IS, Orhan K, Bilgir E, Odabas A, et al. Success of artificial intelligence system in determining alveolar bone loss from dental panoramic radiography images. Cumhuriyet Dent J 2020; 23: 318-24. https://doi.org/10.7126/cumudj.777057
  14. Thanathornwong B, Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Sci Dent 2020; 50: 169-74. https://doi.org/10.5624/isd.2020.50.2.169
  15. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci 2018; 48: 114-23. https://doi.org/10.5051/jpis.2018.48.2.114
  16. Zadrozny L, Regulski P, Brus-Sawczuk K, Czajkowska M, Parkanyi L, Ganz S, et al. Artificial intelligence application in assessment of panoramic radiographs. Diagnostics (Basel) 2022; 12: 224.
  17. Uzun Saylan BC, Baydar O, Yesilova E, Kurt Bayrakdar S, Bilgir E, Bayrakdar IS, et al. Assessing the effectiveness of artificial intelligence models for detecting alveolar bone loss in periodontal disease: a panoramic radiograph study. Diagnostics (Basel) 2023; 13: 1800.
  18. Ryu J, Lee DM, Jung YH, Kwon O, Park S, Hwang J, et al. Automated detection of periodontal bone loss using deep learning and panoramic radiographs: a convolutional neural network approach. Appl Sci(Basel) 2023; 13: 5261.
  19. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics 2017; 37: 2113-31. https://doi.org/10.1148/rg.2017170077
  20. Nguyen TT, Larrivee N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: current clinical trends and research advances. J Can Dent Assoc 2021; 87: I7.
  21. Bordoloi D, Singh V, Sanober S, Buhari SM, Ujjan JA, Boddu R. Deep learning in healthcare system for quality of service. J Healthc Eng 2022; 2022: 8169203.
  22. Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent 2019; 49: 1-7. https://doi.org/10.5624/isd.2019.49.1.1
  23. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19: 1236-46. https://doi.org/10.1093/bib/bbx044
  24. Corbella S, Srinivas S, Cabitza F. Applications of deep learning in dentistry. Oral Surg Oral Med Oral Pathol Oral Radiol 2021; 132: 225-38. https://doi.org/10.1016/j.oooo.2020.11.003
  25. Manerikar CR, Mani A, Mani S, Sachdeva S, Sodhi JK. Convolutional neural network in periodontology - innovative technology or new era? - a review. Ann Rom Soc Cell Biol 2021; 25: 7453-63.
  26. Hou GL, Hung CC, Yang YS, Shieh TY, Tsai CC. Radiographic alveolar bone loss in untreated Taiwan Chinese subjects with adult periodontitis measured by the digital scanning radiographic image analysis method. Dentomaxillofac Radiol 2003; 32: 104-8. https://doi.org/10.1259/dmfr/67162332