Nigerian journal of clinical practice · 2026
Artificial Intelligence-Assisted Analysis of Prevalence Patterns and Phenotypic Distribution of Dental Pulp Stones Using Cone-Beam Computed Tomography Imaging.
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Mahabob N, Madiraju G S, Bello S M, Abraham S
Аннотация
BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into dental radiology to enhance diagnostic accuracy and support epidemiological research. AI-driven techniques, such as K-Means clustering and principal component analysis (PCA) offer novel approaches for analyzing cone-beam computed tomography (CBCT) data.
AIMS: This study applied AI-based clustering techniques to data derived from CBCT images, in a sample of Saudi population, to identify pulp stone prevalence patterns, and evaluate the ability of these methods to reveal hidden patterns and stratify distributional phenotypes.
MATERIALS AND METHODS: Retrospective analysis of 300 CBCT scans (150 males, 150 females) from archives of university dental hospital in Saudi Arabia was conducted. Pulp stones were manually identified, and the CBCT-derived data were analyzed using Chi-square tests and AI-based clustering (K-Means, PCA) to detect prevalence patterns and stratify patient phenotypes.
RESULTS: Pulp stones were identified in 15.3% of patients involving 6.1% of teeth. Significant associations were observed with age (P = 0.027), tooth type (P = 0.036), and dental arch (P = 0.022), but not gender (P = 0.136). AI-based clustering identified three clusters, severe, moderate, and mild, based on prevalence patterns, not captured by conventional analysis.
CONCLUSIONS: AI-based clustering of CBCT-derived data effectively revealed hidden patterns and stratified distributional phenotypes of pulp stones, enhancing epidemiological understanding and supporting personalized, and data-driven decision-making in dental radiology.
AIMS: This study applied AI-based clustering techniques to data derived from CBCT images, in a sample of Saudi population, to identify pulp stone prevalence patterns, and evaluate the ability of these methods to reveal hidden patterns and stratify distributional phenotypes.
MATERIALS AND METHODS: Retrospective analysis of 300 CBCT scans (150 males, 150 females) from archives of university dental hospital in Saudi Arabia was conducted. Pulp stones were manually identified, and the CBCT-derived data were analyzed using Chi-square tests and AI-based clustering (K-Means, PCA) to detect prevalence patterns and stratify patient phenotypes.
RESULTS: Pulp stones were identified in 15.3% of patients involving 6.1% of teeth. Significant associations were observed with age (P = 0.027), tooth type (P = 0.036), and dental arch (P = 0.022), but not gender (P = 0.136). AI-based clustering identified three clusters, severe, moderate, and mild, based on prevalence patterns, not captured by conventional analysis.
CONCLUSIONS: AI-based clustering of CBCT-derived data effectively revealed hidden patterns and stratified distributional phenotypes of pulp stones, enhancing epidemiological understanding and supporting personalized, and data-driven decision-making in dental radiology.