Abstract
Aim
To compare the diagnostic performance of the automated caries detection system (ACDS) for the detection and diagnosis of occlusal caries with the histological appearance of the lesions.
Methods
Eighteen posterior permanent teeth were used, out of which 40 sections were made and 53 areas were evaluated. Teeth with hypoplastic and/or hypomineralised areas or sealants on the occlusal surfaces were excluded from the study. The teeth that were used for this study were a subgroup of the teeth used in the study that introduced ACDS system. This subgroup consisted of teeth having in their occlusal surfaces early carious lesions classified as international caries detection and scoring system (ICDAS) 0, 1, 2 and 3 after clinical examination by the examiners. Histological preparations were classified by experienced examiners based on the Ekstrand, Ricketts and Kidd (ERK) system and for the respective occlusal surfaces by the ACDS system based on ICDAS II system. There were two threshold limits considered as carious in either system ICDAS ≥ 2 or ≥ 3 and ERK index ≥ 2 or ≥ 3 and all possible combinations were analysed. Statistical methods of weighted version of kappa coefficient, Kendall’s tau-b correlation coefficient and p-values using the Fisher’s exact method were used at the confidence level of 0.05.
Results
Intra-examiner kappa coefficient agreement was 0.87 and 0.89 while the inter-examiner for the two trials were 0.87 and 0.92. The ICDAS3-ERK3 combination between the ACDS and histological sections presented the best agreement with kappa coefficient 0.76, agreement 92.5%, sensitivity 100% and specificity 91.1%. ICDAS3-ERK3 combination between the optical examination of the examiners compared to the histological preparations showed kappa coefficient 0.87, agreement 96.2%, sensitivity 100%, Specificity 95.6%.
Conclusion
The evidence supports the view that ACDS classification of occlusal surfaces based on the ICDAS system are comparable with classification to that of an examiner and with the histology of the lesion. The use of ACDS has the distinct advantage though of removing the subjectivity of the examiner since it performs the classification without any intervention by him.
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References
Berdouses ED, Koutsouri GD, Tripoliti EE, et al. A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images. Comput Biol Med. 2015;62:119–35. https://doi.org/10.1016/j.compbiomed.2015.04.016.
Dental Health Foundation Ireland. Dental caries (Tooth decay). 2018. http://www.dentalhealth.ie/dentalhealth/causes/dentalcaries.html. Accessed 24 Nov 2018.
Downer CM. Concurrent validity of an epidemiological diagnostic system for caries with the histological appearance of extracted teeth as validating criterion. Caries Res. 1975;9:231–46.
Ekstrand KR, Ricketts DN, Kidd EA. Reproducibility and accuracy of three methods for assessment of demineralization depth on the occlusal surface: an in vitro examination. Caries Res. 1997;31:224–31.
Ekstrand KR, Ricketts DN, Kidd EA. Occlusal caries: pathology, diagnosis and logical management. Dent Update. 2001;28:380–7.
Ekstrand KR, Ricketts D, Longbottom C, Pitts NB. Visual and tactile assessment of arrested initial enamel carious lesions: an in vivo pilot study. Caries Res. 2005;39:173–7.
Fyffe HE, Deery CH, Nugent ZJ, Nuttall NM, Pitts NB. Effect of diagnostic threshold on the validity and reliability of epidemiological caries diagnosis using the Dundee Selectable Threshold Method for caries diagnosis (DSTM). Comm Dent Oral Epidemiol. 2000;28:42–51.
Ghaedi L, Gottlieb R, Sarrett DC, et al. An automated dental caries detection and scoring system for optical images of tooth occlusal surface. Conf Proc IEEE Eng Med Biol Soc. 2014;1925–8. https://doi.org/10.1109/EMBC.2014.6943988.
Hannigan AOD, Barry D, Schaffer F, Roberts AJ. A caries susceptibility classification of tooth surfaces by survival time. Caries Res. 2000;34:103–8.
International Caries Detection and Assessment System Coordinating Committee. Rationale and Evidence for the International Caries Detection and Assessment System (ICDAS II). 2012. https://www.icdas.org/. Accessed 24 Nov 2018.
Jablonski-Momeni A, Stachniss V, Ricketts DN, Heinzel-Gutenbrunner M, Pieper K. Reproducibility and accuracy of the ICDAS-II for detection of occlusal caries in vitro. Caries Res. 2008;42(2):79–87. https://doi.org/10.1159/000113160.
Jablonski-Momeni A, Ricketts DN, Heinzel-Gutenbrunner M, et al. Impact of scoring single or multiple occlusal lesions on estimates of diagnostic accuracy of the visual ICDAS-II system. Int J Dent. 2009;2009:7 (Article ID 798283).
Kositbowornchai S, Siriteptawee S, Plermkamon S, Bureerat S, Chetchotsak D. An artificial neural network for detection of simulated dental caries. Int J Comput Assist Radiol Surg. 2006;1(2):91–6. https://doi.org/10.1007/s11548-006-0040-x.
Lussi A. Performance of conventional and new methods for the detection of occlusal caries in deciduous teeth. Caries Res. 2003;37:2–7.
Pitts NB, Stamm J. International consensus workshop on caries clinical trials (ICW-CCT)—final consensus statements: agreeing where the evidence leads. J Dent Res. 2004;83(suppl 1):C125–8.
Ricketts DN, Ekstrand KR, Kidd EA, Larsen T. Relating visual and radiographic ranked scoring systems for occlusal caries detection to histological and microbiological evidence. Oper Dent. 2002;27:231–7.
Ripa LW, Leske GS, Sposato A. The surface-specific caries pattern of participants in a school-based fluoride mouthrinsing program with implications for the use of sealants. J Pub Health Dent. 1985;45(2):90–5.
Sawle R, Andlaw RJ. Has occlusal caries become difficult to diagnose? A study comparing clinically undetected lesions in molar teeth of 14-16-year old children in 1974 and 1982. Br Dent J. 1988;164(7):209–11.
Selwitz R, Ismail AI, Pitts NB. Dental caries. Lancet. 2007;369(9555):51–9.
Stookey GK. In Stookey GK, editor, Proceedings of the first annual indiana conference: early detection of dental caries. Indianapolis: Indiana University Press; 1996.
Stookey GK. In Stookey GK, editor, Proceedings of the Second International Indiana Co. Indianapolis: Indiana University Press; 2000.
Stookey GK. In Stookey GK, editor, Early caries detection III, 2004; Indianapolis: Indiana University Press.
Umemori S, Tonami K, Nitta H, Mataki S, Araki K. The possibility of digital imaging in the diagnosis of caries. Int J Dent. 2010. https://doi.org/10.1155/2010/860515.
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Berdouses, E.D., Oulis, C.J., Michalaki, M. et al. Histological validation of the automated caries detection system (ACDS) in classifying occlusal caries with the ICDAS II system in vitro. Eur Arch Paediatr Dent 20, 249–255 (2019). https://doi.org/10.1007/s40368-018-0389-x
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DOI: https://doi.org/10.1007/s40368-018-0389-x