Instant identification of dental white spot using K-means algorithm via laser-induced fluorescence and associated...

Dental caries (DC) is a chronic illness that affects 2 billion individuals worldwide, with 520 million of those suffering from it in their primary teeth. It is apparent that early DC detection and subsequent minimally invasive therapy are crucial clinical requirements. The first reversible clinical indication of demineralization is dental white spot (DWS) lesions. However, diagnosing DWS poses extreme challenges for practitioners. In this investigation, a customized laser-induced fluorescence system with a hyperspectral imaging (HI) camera and a non-ionization laser light supply was created for DWS localization and early DC detection. A UV laser diode source with a wavelength of 395 nm was used for light stimulation for the 10 test samples of teeth. The emitted signature of the main tooth components, including dentin, DWS, enamel, and DC, was recorded. An attempt was made to increase the system's sensitivity to the fluorescent signal by applying a logarithmic scale to the spectral signature. Moreover, further discrimination may be achieved by signal strength. We identified that the fluorescent signal's peak intensity at 771 nm works best for discriminating DWS from normal areas, or DC. For characterizing dentin, the re-emitted frequency at 500 nm has the maximum intensity. Next, we presented our imaging grouping strategy that combines visual enhancement through a moving average, MA, filtering and segmenting an image using K-means clustering (K-mc) (K = 8) for instant and precise DWS grouping for the constructed HI images at (500 nm and 771 nm). Despite the tiny structure and its DWS white appearance, our approach could successfully demarcate the DWS on the tested teeth. Dental examiners might benefit from our simple, non-invasive, non-ionizing optical diagnosis approach to help them make their first assessments and experience accurate and exact delineation of the DWS to obtain immediate and higher rates of early-stage DC detection.