Dental tooth surface classification
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Dental Tooth Surface Classification: Methods and Approaches
Traditional and Updated Classification Systems for Tooth Surfaces
Tooth surface classification has long relied on established systems, such as the G.V. Black classification, which categorizes carious lesions based on their location and extent on exposed tooth surfaces. Despite its age, this system remains widely used in clinical practice. However, advances in understanding the causes and progression of dental caries have led to calls for updated classification systems that better reflect the dynamic nature of caries and the importance of minimal intervention and preventive care . Additionally, new nomenclature and definitions for tooth surface lesions—such as attrition, abrasion, corrosion, and abfraction—have been proposed to standardize terminology and improve clinical communication and diagnosis .
Classification of Tooth Surface Lesions: Erosion, Abrasion, and Caries
Tooth surface loss can result from various non-carious processes, including erosion, abrasion, and their combinations. Differentiating these lesions is important for effective management. Surface texture analysis using parameters like average surface roughness (Sa), area-scale fractal complexity (Asfc), and textural fill volume (Tfv) has been shown to distinguish between erosion, abrasion, and erosion-abrasion lesions on both enamel and dentin. Combining these parameters can improve classification accuracy, with correct identification rates reaching up to 94% for dentin and 84% for enamel . Updated classification systems also emphasize the need to understand the underlying mechanisms of tooth surface loss to guide prevention and treatment Hanif2015Grippo2004.
Caries Susceptibility and Survival-Based Classification
Tooth surfaces vary in their susceptibility to caries, and this risk changes over time. Cluster analysis of survival time data (the time until a surface becomes decayed or filled) allows for grouping tooth surfaces by caries susceptibility. This method provides a descriptive measure that can be applied to longitudinal studies, helping to identify high-risk surfaces and inform targeted preventive strategies .
Classification of Surface Defects in Gingival Recession
A specific classification system for dental surface defects in areas of gingival recession considers the presence or absence of the cemento-enamel junction (CEJ) and the presence or absence of a step caused by abrasion. This system identifies four classes (A+, A-, B+, B-) and has demonstrated high intra-rater reliability, making it useful for diagnosing and managing gingival recession .
Automated and Deep Learning-Based Tooth Surface Classification
Recent advances in digital dentistry have enabled the use of artificial intelligence for automated tooth surface classification. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) can classify tooth types and specific features, such as cusps, from 3D scan data with high accuracy (over 90%) Chen2021Raith2016Tian2019. Hierarchical deep learning models further improve classification by addressing the challenge of distinguishing between highly similar tooth categories. These models can segment and classify teeth on 3D dental models, supporting efficient and accurate computer-assisted diagnosis and treatment planning . Object detection models like YOLO have also been used for multi-granularity tooth analysis, showing that classification accuracy decreases as the number of classes increases, but still achieving high precision at lower granularity levels .
Conclusion
Dental tooth surface classification encompasses a range of approaches, from traditional systems and clinical criteria to advanced digital and AI-based methods. Modern techniques, including surface texture analysis and deep learning, offer improved accuracy and automation, supporting better diagnosis, prevention, and management of dental diseases and surface defects. As technology advances, these methods are likely to become increasingly integrated into routine dental practice, enhancing both clinical outcomes and workflow efficiency Hara2016Chen2021Hannigan2000+6 MORE.
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