Caries measurement methods vary considerably in terms of the stages of lesion considered making the comparison problematic among different surveys. In this cross-sectional study, four caries measurement methods, the WHO-DMFT, the International Caries Detection and Assessment System (ICDAS), the Caries Assessment Spectrum and Treatment (CAST), and the Nyvad Criteria were tested in a sample of children. Five-hundred 12-year old children (236 males and 264 females) were examined four times by four calibrated examiners. The calibration process showed that Cohen's Kappa exceeded the criterion of K = 0.75 and K = 0.80 for inter/intra-examiner agreement, respectively. In the survey, the total number of misclassification errors for the four methods amounted to 312 observations (67.94% regarding enamel lesions). The greatest difference among methods was shown by number of sound teeth ( < 0.01): WHO-DMFT = 9505, 74.14%; ICDAS = 2628, 20.49%; CAST = 5053, 39.41%; and Nyvad Criteria = 4117, 32.11%. At the level of dentinal Distinct/Active Cavity lesions, no statistically significant difference was observed ( = 0.40) between ICDAS ( = 1373, 10.71%), CAST ( = 1371, 0.69%), and Nyvad Criteria ( = 1720, 13.41%). In the severe caries levels, all methods were partially in agreement, while no accordance was found for the initial (enamel) lesions. A common language in caries detection is critical when different studies are compared.
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http://dx.doi.org/10.3390/ijerph16214120 | DOI Listing |
Int J Clin Pediatr Dent
November 2024
Department of Pediatric and Preventive Dentistry, Yenepoya Dental College, Mangaluru, Karnataka, India.
Aim And Background: The applications of artificial intelligence (AI) are escalating in all frontiers, specifically healthcare. It constitutes the umbrella term for a number of technologies that enable machines to independently solve problems they have not been programmed to address. With its aid, patient management, diagnostics, treatment planning, and interventions can be significantly improved.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Department of Operative Dentistry, Postgraduate Program in Dentistry, Faculty of Pharmacy, Dentistry and Nursing, Federal University of Ceará, Fortaleza, Ceará, Brazil.
Objectives: This cross-sectional study aimed to evaluate the occurrence of Streptococcus spp., Streptococcus mutans, its serotypes (c, e, f, and k), collagen-binding genes (cnm/cbm), and Candida albicans in medium deep (D2) and deep (D3) dentin carious lesions of permanent teeth.
Materials And Methods: Carious dentin was collected from D2 (n = 23) and D3 (n = 24) lesions in posterior teeth from 31 individuals.
Children (Basel)
November 2024
Department of Pediatric Dentistry, Faculty of Dental Medicine, Hebrew University, Hadassah Medical Center, P.O. Box 12272, Jerusalem 91120, Israel.
Objectives: The present prospective study aimed to compare near-infrared light reflection (NIRI) and bitewing radiographs (BWR) images to detect proximal caries in primary teeth.
Methods: 71 children underwent routine BWR, and scans were performed using an intra-oral scanner (iTero Element 5D, Align Technology, Tempe, AZ, USA), including a near-infrared light source (850 nm) and sensor. Five specialist pediatric dentists examined the NIRI and BWR images.
Diagnostics (Basel)
December 2024
Department of Oral Diagnosis, Faculty of Dental Medicine, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania.
Artificial intelligence (AI) is revolutionizing the field of oral and dental healthcare by offering innovative tools and techniques for optimizing diagnosis, treatment planning, and patient management. This narrative review explores the current applications of AI in dentistry, focusing on its role in enhancing diagnostic accuracy and efficiency. AI technologies, such as machine learning, deep learning, and computer vision, are increasingly being integrated into dental practice to analyze clinical images, identify pathological conditions, and predict disease progression.
View Article and Find Full Text PDFBDJ Open
January 2025
Professor of Conservative Dentistry, Faculty of Dentistry, Cairo University, Giza, Egypt.
Objectives: To assess the validity of light-induced and laser-induced fluorescence devices compared to the visual-tactile method for detecting secondary caries around resin composite restorations.
Materials And Methods: The study included 20 participants with 30 resin-composite restored teeth. Restorations' margins were examined using three diagnostic methods: the visual-tactile method (FDI criteria), the light-induced fluorescence camera (VistaCam iX), and the laser-induced fluorescence device (DIAGNOdent pen), and the reference was visual inspection after removal of defective restorations.
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