Applicability and performance of convolutional neural networks for the identification of periodontal bone loss in periapical radiographs: a scoping review

Shi B, Chang M, Martin J, Mitreva M, Lux R, Klokkevold P, et al. Dynamic changes in the subgingival microbiome and their potential for diagnosis and prognosis of periodontitis. MBio. 2015;6:1–11. https://doi.org/10.1128/mbio.01926-14.

Article  Google Scholar 

Hajishengallis G, Chavakis T, Lambris JD. Current understanding of periodontal disease pathogenesis and targets for host-modulation therapy. Periodontology. 2020;84(1):14–34. https://doi.org/10.1111/prd.12331.

Article  PubMed  PubMed Central  Google Scholar 

Huang X, Xie M, Xie Y, Mei F, Lu X, Li X, et al. The roles of osteocytes in alveolar bone destruction in periodontitis. J Transl Med. 2020;18:1–15. https://doi.org/10.1186/s12967-020-02664-7.

Article  CAS  Google Scholar 

Tonetti MS, Greenwell H, Kornman KS. Staging and grading of periodontitis: framework and proposal of a new classification and case definition. J Clin Periodontol. 2018;45:S149–61. https://doi.org/10.1002/jper.18-0006.

Article  PubMed  Google Scholar 

Papapanou PN, Sanz M, Buduneli N, Dietrich T, Feres M, Fine DH, et al. Periodontitis: consensus report of workgroup 2 of the 2017 World Workshop on the classification of periodontal and peri-implant diseases and conditions. J Clin Periodontol. 2018;45:S162–70. https://doi.org/10.1111/jcpe.12946.

Article  PubMed  Google Scholar 

Jacobs R, Fontenele RC, Lahoud P, Shujaat S, Bornstein MM. Radiographic diagnosis of periodontal diseases–current evidence versus innovations. Periodontology. 2024;95(1):51–69. https://doi.org/10.1111/prd.12580.

Article  PubMed  Google Scholar 

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. 2017;74(1):116–39. https://doi.org/10.1111/prd.12193.

Article  PubMed  Google Scholar 

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;9:1–6. https://doi.org/10.1038/s41598-019-44839-3.

Article  CAS  Google Scholar 

Winkler P, Dannewitz B, Nickles K, Petsos H, Eickholz P. Assessment of periodontitis grade in epidemiological studies using interdental attachment loss instead of radiographic bone loss. J Clin Periodontol. 2022;49:854–61. https://doi.org/10.1111/jcpe.13679.

Article  PubMed  Google Scholar 

Hardy M, Harvey H. Artificial intelligence in diagnostic imaging: Impact on the radiography profession. Br J Radiol. 2020;93(1108):20190840. https://doi.org/10.1259/bjr.20190840.

Article  PubMed  PubMed Central  Google Scholar 

Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology. 2019;290:590–606. https://doi.org/10.1148/radiol.2018180547.

Article  PubMed  Google Scholar 

Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofacial Radiol. 2022;51(1):20210197. https://doi.org/10.1259/dmfr.20210197.

Article  Google Scholar 

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. Imag Sci Dent. 2022;52:383–91. https://doi.org/10.5624/isd.20220105.

Article  Google Scholar 

Patil S, Joda T, Soffe B, Awan KH, Fageeh HN, Tovani-Palone MR, et al. Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases: a systematic review. J Am Dent Assoc. 2023;154:795–804. https://doi.org/10.1016/j.adaj.2023.05.010.

Article  PubMed  Google Scholar 

Stera G, Giusti M, Magnini A, Calistri L, Izzetti R, Nardi C. Diagnostic accuracy of periapical radiography and panoramic radiography in the detection of apical periodontitis: a systematic review and meta-analysis. Radiol Med. 2024;129:1682–95. https://doi.org/10.1007/s11547-024-01882-z.

Article  PubMed  PubMed Central  Google Scholar 

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467–73. https://doi.org/10.7326/m18-0850.

Article  PubMed  Google Scholar 

Lee J, Kim D, Jeong S-N, Choi S-H. 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.

Article  PubMed  PubMed Central  Google Scholar 

Moran MBH, Faria M, Giraldi G, Bastos L, Da Silva IB, Conci A. On using convolutional neural networks to classify periodontal bone destruction in periapical radiographs. Proc – 2020 IEEE Int Conf Bioinforma Biomed BIBM. 2020;2020:2036–9. https://doi.org/10.1109/BIBM49941.2020.9313501.

Yavuz MB, Sali N, Kurt Bayrakdar S, Ekşi C, İmamoğlu BS, Bayrakdar İŞ, et al. Classification of periapical and bitewing radiographs as periodontally healthy or diseased by deep learning algorithms. Cureus. 2024. https://doi.org/10.7759/cureus.60550.

Article  PubMed  PubMed Central  Google Scholar 

Khan HA, Haider MA, Ansari HA, Ishaq H, Kiyani A, Sohail K, et al. Automated feature detection in dental periapical radiographs by using deep learning. Oral Surg Oral Med Oral Pathol Oral Radiol. 2021;131:711–20. https://doi.org/10.1016/j.oooo.2020.08.024.

Article  PubMed  Google Scholar 

Moran M, Faria M, Giraldi G, Bastos L, Conci A. Do radiographic assessments of periodontal bone loss improve with deep learning methods for enhanced image resolution? Sensors. 2021;21:1–24. https://doi.org/10.3390/s21062013.

Article  Google Scholar 

Alotaibi G, Awawdeh M, Farook FF, Aljohani M, Aldhafiri RM, Aldhoayan M. Artificial intelligence (AI) diagnostic tools: utilizing a convolutional neural network (CNN) to assess periodontal bone level radiographically—a retrospective study. BMC Oral Health. 2022;22:1–7. https://doi.org/10.1186/s12903-022-02436-3.

Article  Google Scholar 

Tsoromokos N, Parinussa S, Claessen F, Moin DA, Loos BG. Estimation of alveolar bone loss in periodontitis using machine learning. Int Dent J. 2022;72:621–7. https://doi.org/10.1016/j.identj.2022.02.009.

Article  PubMed  PubMed Central  Google Scholar 

Chang J, Chang MF, Angelov N, Hsu CY, Meng HW, Sheng S, et al. Application of deep machine learning for the radiographic diagnosis of periodontitis. Clin Oral Investig. 2022;26:6629–37. https://doi.org/10.1007/s00784-022-04617-4.

Article  PubMed  Google Scholar 

Chen CC, Wu YF, Aung LM, Lin JCY, Ngo ST, Su JN, et al. Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence. J Dent Sci. 2023;18:1301–9. https://doi.org/10.1016/j.jds.2023.03.020.

Article  PubMed  PubMed Central  Google Scholar 

Hoss P, Meyer O, Wölfle UC, Wülk A, Meusburger T, Meier L, et al. Detection of periodontal bone loss on periapical radiographs—a diagnostic study using different convolutional neural networks. J Clin Med. 2023;12(22):7189. https://doi.org/10.3390/jcm12227189.

Article  PubMed  PubMed Central  Google Scholar 

Chen IH, Lin CH, Lee MK, Chen TE, Lan TH, Chang CM, et al. Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph. J Dent Sci. 2024;19:550–9. https://doi.org/10.1016/j.jds.2023.09.032.

Article  PubMed  Google Scholar 

Armitage GC. Development of a classification system for periodontal diseases and conditions. Ann Periodontol. 1999;4(1):1–6. https://doi.org/10.1902/annals.1999.4.1.1.

Article  CAS  PubMed  Google Scholar 

Rainio O, Teuho J, Klén R. Evaluation metrics and statistical tests for machine learning. Sci Rep. 2024;14:1–14. https://doi.org/10.1038/s41598-024-56706-x.

Article  CAS  Google Scholar 

Müller DP, Pereira S, Alves V. Towards a guideline for evaluation metrics in medical image segmentation. BMC Res Notes. 2022;15:102. https://doi.org/10.1186/s13104-022-06096-y.

Article  Google Scholar 

Kornman KS, Papapanou PN. Clinical application of the new classification of periodontal diseases: ground rules, clarifications, and “gray zones.” J Periodontol. 2020;91:352–60. https://doi.org/10.1002/JPER.19-0671.

Article  CAS  PubMed  Google Scholar 

Salvi GE, Roccuzzo A, Imber JC, Stähli A, Klinge B, Lang NP. Clinical periodontal diagnosis. Periodontol. 2000;2023:1–19. https://doi.org/10.1111/prd.12487.

Eickholz P, Hausmann E. Accuracy of radiographic assessment of interproximal bone loss in intrabony defects using linear measurements. Eu

Comments (0)

No login
gif