Introduction: Initial proximal caries is both diagnostic and therapeutic challenge. The disadvantages of the conventional methods for caries detection and the development of technologies led to the creation of contemporary optical devices for early caries detection.
Aim: In vitro comparison of the diagnostic accuracy of several methods for early proximal caries detection - visual-tactile, bitewing radiography and laser fluorescence device (DIAGNOdent pen).
Materials And Methods: Fifty-eight proximal surfaces of extracted human permanent premolars and molars were examined by two examiners using visual inspection, bitewing radiography, DIAGNOdent with proximal contact, and DIAGNOdent directly in the lesion. Results were compared with the histological gold standard. Statistical analysis with ROC curve, sensitivity, specificity and diagnostic accuracy of each detection method was performed. Analysis was conducted in 3 diagnostic thresholds - initial, developed and advanced demineralization.
Results: Sensitivity of visual inspection was 16%-33%, specificity 93.3%-100%, sensitivity of bitewing radiography 54%-67%, speci-ficity 93%-94%, sensitivity of DIAGNOdent with proximal surfaces in contact 88%-91%, specificity 79%-89%, sensitivity of DIAG-NOdent directly 89%-92.5%, specificity 81.29%-93%. The highest diagnostic accuracy, increasing with the rise of the level of demin-eralization, was shown by DIAGNOdent directly, followed by DIAGNOdent with proximal contact, bitewing radiography, and visual inspection with the lowest accuracy.
Conclusion: The use of contemporary diagnostic devices significantly increases the possibility for early detection of proximal lesions. DIAGNOdent can be used as an adjunct to and increasing the diagnostic accuracy of the conventional caries detection methods.
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http://dx.doi.org/10.3897/folmed.62.e47534 | 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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