## 4 Discussion
According to [PERSON] and [PERSON] [52], an increased L* value can lead to more pronounced color mismatches, which may affect the clinical accuracy of shade matching. The high L* value observed in MC-DSG was likely due to the larger pixel areas on the full-frame sensor of a mirrorless camera, which enhances light capture [53] as compared to a smartphone camera. Additionally, the use of ISO settings above the commonly used baseline of 100 [19; 54] in MC-DSG compensates for the low lighting conditions but also results in increased image exposure. Therefore, fine-tuning the ISO setting is necessary for more accurate color validation in future studies. In this study, all three study groups demonstrated excellent intra-device repeatability. Nevertheless, the spectrophotometer proved to be a more reliable shade-matching instrument as compared to SC-DSG and MC-DSG. The superior reliability of the spectrophotometer is likely attributed to its use of an internal light source, as opposed to the standardized external ambient light sources used by the SC-DSG and MC-DSG. This observation is further supported by [PERSON] et al. [55], who reported that the spectrophotometer exhibited almost perfect reliability when using the device's light source alone or in combination with room lighting. A two-step white balance calibration process was employed in this study to enhance the color accuracy of each photographed shade tab. This involved using a white balance gray card for calibration both within the photographic device and during the image processing phase. The manual white balance calibration method was preferred in this study as it minimizes the influence of undesirable environmental light and significantly improves digital color accuracy compared to automatic white balance correction [56]. These findings align with those of [PERSON] et al. [36], who reported a 26% improvement in shade-matching accuracy when using a digital shade guide with a custom white balance. Furthermore, post-processing white balance adjustments in Photoshop further refine color accuracy, allowing for precise fine-tuning of the image to achieve optimal color representation, which is particularly valuable when dealing with complex lighting conditions [57; 58]. In recent years, numerous mobile applications for shade matching have been introduced in clinical dentistry, providing a convenient means to assist visual shade selection. However, many of these applications rely heavily on in-app camera settings and ambient lighting conditions, which may introduce inconsistencies in color capture [6]. In contrast, this study used standardized photography parameters, cross-polarizing filters, and post-processing calibration to improve color accuracy and reproducibility in clinical shade assessment [16; 59]. Several limitations have been identified in this study. Only one type of digital camera and smartphone camera was used for constructing image-assisted digital shade guides. Future studies are needed to explore the effectiveness of image-assisted digital shade guides constructed by different types of cameras with standardized photography parameters on shade matching accuracy, especially in clinical settings. This study used standardized camera settings, cross-polarizing filters, and a WhiBal gray card to set a custom white balance for each photography session. While this practical calibration helps reduce color variation, full device-specific camera profiling was not performed. This may limit cross-device consistency and highlights the need for more advanced calibration methods in future studies to ensure consistent color capture across devices. Additionally, the in vitro setting may not fully replicate clinical lighting conditions, and shade matching on natural teeth may introduce further variables. Moreover, the color measurement on the incisal and gingival third of the shade tab should be taken into consideration for a more comprehensive color accuracy assessment.