## 4 Discussion  
Based on HSI measurements, the occlusal surfaces of posterior teeth were classified into stained but sound enamel and stained lesions using a classification algorithm of artificial intelligence, and its classification performance was validated with PLM. The classification model was subsequently used to classify the enamel health of further teeth's occlusal surfaces, the results of which were color-coded and mapped onto an occlusal projection of each respective tooth, allowing immediate assessment of large parts of its occlusal fissural system. This study used a wide range of wavelengths, unlike previous research focusing on intensities at specific wavelengths [12]. As dental hard tissue possesses no specific absorption bands in the 505-1000 nm range [40], this approach may aid classification performance. Stains can be detected in the employed wavelength range and there is little interference from water bands, which occur above 1450 nm [41; 42; 43]. Previous research suggests that stains appear transparent beyond 1150 nm [41; 44], and hence no longer confound demineralization detection. This study, however, suggests that stains may appear somewhat transparent at lower NIR wavelengths already, as the underlying demineralization present in stained lesions became visible at wavelengths over approximately 800 nm (see Figure 3), which possibly originates from the increased scattering of incipient demineralizations. Nonetheless, the performance of differentiating stains from stained lesions by means of classification algorithms in the wavelength range 1000-2000 nm would be of great interest. However, costlier InGaAs sensors are then required, which could impede widespread clinical adoption. Further research is also required to investigate the transparency of stains of different origins in the NIR. The teeth in this study were free of plaque, saliva and blood. For clinical application, these complicating circumstances must be considered. The effect of plaque, saliva and blood present on a tooth's occlusal surface on the feasibility and quality of hyperspectral imaging and classification performance needs to be assessed. The results in this study are based on the simplified assumption of a homogeneous distribution of the occlusal health state that was histologically determined at the central region of the fissure. Occlusal caries originates from the fissure in the vast majority of cases. The part of the fissure that clinical experience showed to be the most questionable part (i.e., the most likely to be curious) was selected for further analysis in this study, as this is the clinically most relevant issue. As caries is a dynamic process, however, occlusal classification images show heterogeneous enamel health states (Table 3). Lesions may develop in one or more sites of the fissure and subsequently spread to further areas. Hence, it is likely that some parts of an analyzed tooth are still unaffected, whereas others contain lesions of varying extent. Thus, teeth showing sound but stained enamel in the histological cross-sectional plane may show lesions in other parts of their fissure (Table 3a,b). On the other hand, histologically curious teeth show still unaffected fissural regions (Table 3c,e). In order to refine the validation process, further cross-sections from different parts of the tooth's fissural system should be considered. Occlusal caries originating from outside the fissure are rare and pose no pertinent clinical issue. In close proximity to the histologically validated areas, the HSI-based classification results show very good agreement with the reference occlusal health state determined by PLM of ground sections (\(MCC=0.75\)). The uneven distribution of samples between stained but intact and stained demineralized teeth hinders the use of traditional confusion matrix-based measures such as sensitivity, specificity, the F1-Score or accuracy, as these can encounter strong bias from imbalanced data [45].