Artificial Intelligence–Driven Detection of Subclinical Pulpal Degeneration Using Multispectral Optical Signals

Abstract

Early-stage pulpal degeneration often progresses without overt clinical symptoms, limiting the effectiveness of conventional
diagnostic methods that rely on subjective assessment or late-stage structural changes. This study presents an artificial
intelligence–driven framework for the detection of subclinical pulpal degeneration using multispectral optical signals. By
leveraging the differential interaction of optical wavelengths with dental tissues, multispectral data capture subtle biochemical
and physiological variations associated with early pulpal changes. Advanced machine learning and deep learning
techniques are employed to extract discriminative features from the optical signals and to classify pulpal status with high
sensitivity and specificity. The proposed approach demonstrates the potential to identify degenerative changes prior to
clinical manifestation, supporting earlier intervention and more conservative treatment planning. This AI-based diagnostic
paradigm highlights a shift toward data-driven, non-invasive, and objective assessment of pulpal health, with implications
for improving diagnostic accuracy and advancing precision dentistry.