Classification of occlusal caries severity using spectrophotometric CIELAB measurements and machine learning algorithms

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Deep Sets model achieved balanced accuracy of 0.89 for multiclass classification

• Primary Outcome: Multiclass caries severity classification accuracy

• Nnt Or Sensitivity: 100% sensitivity for operative lesion detection with MLP model, but specificity not clearly reported

• Confidence Interval: Not reported

Clinical Application

Requires specialized spectrophotometric equipment and machine learning infrastructure Would require TGA approval for clinical use; no current RACGP guidelines for spectrophotometric caries detection; potential research opportunity for Australian dental schools Limited applicability - study conducted on extracted teeth under laboratory conditions

Abstract

OBJECTIVES: This study evaluated whether spectrophotometric CIELAB color measurements can support machine-learning (ML) classification of occlusal caries severity based on ICDAS categories. METHODS: Three hundred extracted human teeth were visually classified using ICDAS (0-4) and served as labels for supervised learning. Five occlusal sites per tooth were measured using a spectrophotometer under standardized conditions. Site-level CIELAB values were transformed into engineered color features and aggregated at the tooth level. Teeth were categorised as sound (ICDAS 0), initial carious lesions (1-2), and moderate carious lesions (3-4). Five ML models: Random Forest (RF), XGBoost, CatBoost, Multilayer Perceptron (MLP), and a Deep Sets architecture were trained. Performance was evaluated using accuracy, balanced accuracy (BA), and macro-F1, with additional binary analyses for early (ICDAS 1-4 vs. 0) and operative lesion detection (ICDAS 3-4 vs. 0-2), based on the included ICDAS range. Learning curve analysis was performed to evaluate the effect of training data size on model performance. RESULTS: Deep Sets achieved the highest multiclass performance (BA = 0.89) followed by the MLP (BA = 0.72). Tree-based models demonstrated lower performance overall. For early lesion detection, all models showed high sensitivity (SE) but reduced specificity (SP). At the operative level, the MLP achieved 100% SE and moderate SP. Learning-curve analysis showed that neural models benefited most from increased training data, whereas tree-based models showed limited improvement. CONCLUSION: Under controlled in-vitro conditions, spectrophotometric CIELAB measurements enabled machine-learning classification of occlusal caries severity. Deep sets achieved the highest overall performance, supporting the potential of color-based approaches as adjunctive tools for caries assessment. CLINICAL RELEVANCE: Spectrophotometric tooth color measurements may support machine learning classification of occlusal caries severity under controlled conditions, using an image-independent approach. Such color-based methods may complement visual assessment by providing a more standardized and reproducible evaluation of enamel changes following further validation.

References

1. 1.Rashid, F., & Dudley, J. (2026). Classification of occlusal caries severity using spectrophotometric CIELAB measurements and machine learning algorithms. Clinical Oral Investigations. https://doi.org/10.3390/bioengineering10020245