Validity and Reproducibility of Different Combinations of Methods for Occlusal Caries Detection: An in vitro Comparison

2006 ◽  
Vol 40 (3) ◽  
pp. 194-201 ◽  
Author(s):  
W.C. Souza-Zaroni ◽  
J.C. Ciccone ◽  
A.E. Souza-Gabriel ◽  
R.P. Ramos ◽  
S.A.M. Corona ◽  
...  
Keyword(s):  
Caries Detection ◽  
Occlusal Caries ◽  
Occlusal Caries Detection ◽  
In Vitro Comparison

Occlusal Caries Detection with KaVo DIAGNOdent and Radiography: An in vitro Comparison

2000 ◽  
Vol 34 (2) ◽  
pp. 151-158 ◽  
Author(s):  
X.-Q. Shi ◽  
U. Welander ◽  
B. Angmar-Månsson
Keyword(s):  
Caries Detection ◽  
Occlusal Caries ◽  
Occlusal Caries Detection ◽  
In Vitro Comparison

scholarly journals An in vitro Comparison between Two Methods of Electrical Resistance Measurement for Occlusal Caries Detection

2006 ◽  
Vol 40 (2) ◽  
pp. 104-111 ◽  
Author(s):  
J. Kühnisch ◽  
R. Heinrich-Weltzien ◽  
M. Tabatabaie ◽  
L. Stösser ◽  
M.C.D.N.J.M. Huysmans
Keyword(s):  
Electrical Resistance ◽  
Resistance Measurement ◽  
Caries Detection ◽  
Electrical Resistance Measurement ◽  
Occlusal Caries ◽  
Occlusal Caries Detection ◽  
In Vitro Comparison

scholarly journals Automated Caries Detection using a 3D Intraoral Scanner. An in Vivo Validation Study

2021 ◽  
Author(s):  
Stavroula Michou ◽  
Mathias S. Lambach ◽  
Panagiotis Ntovas ◽  
Ana R. Benetti ◽  
Azam Bakhshandeh ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Periodontal Diseases ◽  
Caries Detection ◽  
Oral Diseases ◽  
Visual Examination ◽  
Occlusal Caries ◽  
Tooth Color ◽  
Occlusal Caries Detection

Abstract The use of 3D intraoral scanners (IOS) and software that can support automated detection and objective monitoring of oral diseases such as caries, tooth wear or periodontal diseases is increasingly receiving attention from researchers and industry. This study clinically validates an automated caries scoring system for occlusal caries detection and classification, previously developed for an IOS system featuring fluorescence (TRIOS 4, 3Shape TRIOS A/S, Denmark). Four algorithms (ALG1, ALG2, ALG3, ALG4) are assessed for the IOS; the first three are based only on fluorescence information, while ALG4 also takes into account the tooth color information. The diagnostic performance of these automated algorithms is compared with the diagnostic performance of the clinical visual examination, while histological assessment is used as reference. Additionally, possible differences between in vitro and in vivo diagnostic performance of the IOS system are investigated. The algorithms show comparable in vivo diagnostic performance to the visual examination. Only minor differences between their in vitro and in vivo diagnostic performance are noted. This novel IOS system exhibits encouraging performance for clinical application on occlusal caries detection and classification. Different approaches can be investigated for possible optimization of the system.

Near-infrared transillumination with high dynamic range imaging for occlusal caries detection in vitro

2020 ◽  
Vol 35 (9) ◽  
pp. 2049-2058
Author(s):  
Friederike Litzenburger ◽  
Alexander Lederer ◽  
Maximilian Kollmuß ◽  
Reinhard Hickel ◽  
Karl-Heinz Kunzelmann ◽  
...  
Keyword(s):  
Near Infrared ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Caries Detection ◽  
High Dynamic Range Imaging ◽  
Range Imaging ◽  
Occlusal Caries ◽  
Occlusal Caries Detection ◽  
High Dynamic

scholarly journals Automated caries detection in vivo using a 3D intraoral scanner

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stavroula Michou ◽  
Mathias S. Lambach ◽  
Panagiotis Ntovas ◽  
Ana R. Benetti ◽  
Azam Bakhshandeh ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Periodontal Diseases ◽  
Roc Curves ◽  
Caries Detection ◽  
Visual Examination ◽  
Occlusal Caries ◽  
Occlusal Caries Detection ◽  
Significant Difference

AbstractThe use of 3D intraoral scanners (IOS) and software that can support automated detection and objective monitoring of oral diseases such as caries, tooth wear or periodontal diseases, is increasingly receiving attention from researchers and industry. This study clinically validates an automated caries scoring system for occlusal caries detection and classification, previously defined for an IOS system featuring fluorescence (TRIOS 4, 3Shape TRIOS A/S, Denmark). Four algorithms (ALG1, ALG2, ALG3, ALG4) are assessed for the IOS; the first three are based only on fluorescence information, while ALG4 also takes into account the tooth color information. The diagnostic performance of these automated algorithms is compared with the diagnostic performance of the clinical visual examination, while histological assessment is used as reference. Additionally, possible differences between in vitro and in vivo diagnostic performance of the IOS system are investigated. The algorithms show comparable in vivo diagnostic performance to the visual examination with no significant difference in the area under the ROC curves ($$p>0.05$$ p > 0.05 ). Only minor differences between their in vitro and in vivo diagnostic performance are noted but no significant differences in the area under the ROC curves, ($$p>0.05$$ p > 0.05 ). This novel IOS system exhibits encouraging performance for clinical application on occlusal caries detection and classification. Different approaches can be investigated for possible optimization of the system.

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