Non-destructive classification of type 2 diabetic teeth using LIBS-derived elemental biomarkers and machine learning

Type 2 diabetes mellitus (T2DM) is a chronic metabolic illness that severely alters oral health, elevating periodontal infection incidence and dental treatment failures via mechanisms including hyperglycemia. While laser-induced breakdown spectroscopy (LIBS) using machine learning (ML) has demonstrated the presence of diabetes in hair, nails, and urine, mineralized tooth tissues that preserve long-term metabolic signatures remain underexplored as a diagnostic substrate.

A total of 3600 LIBS spectra were collected from four dental tissues (enamel, coronal dentine, radicular dentine, cementum) obtained from 30 individuals (15 healthy and 15 diabetic). LIBS elemental analysis showed higher Fe and Sn in diabetic teeth, along with lower Zn, Si, and K, indicating they are diabetes-related biomarkers. Among the various ML models tested (LR, SVM, ANN) using PCA and correlation-based feature selection (CFS-BFS), the PCA-ANN achieved the greatest performance: mean accuracy 96%, mean sensitivity 94%, and mean specificity 96%. However, PCA-SVM (accuracy 95%) with a narrow confidence interval (94.98-95.02) %, makes it the most stable model in terms of accuracy. No single model was consistently the most stable across all performance evaluation metrics. PCA with 6 Pcs captured 94% of the variance, consistently outperforming CFS-BFS integrated ML algorithms.

This work confirms that dental tissues preserve diabetes-induced elemental alterations detectable by LIBS-ML, thus enabling a non-destructive screening method for undiagnosed diabetes in dental practice. By providing pre-treatment risk identification, the LIBS technique can potentially reduce dental treatment failures and improve patient outcomes. Large-scale multicenter validation studies are, however, required to convert these results into therapeutic practice.