# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 4 Discussion  
In 2014, [PERSON] et al. [33] reported that there was no significant difference between the visual assessment of tongue photographs and the patient's unstimulated salivary flow rate (USFR) or xerostomia inventory (XI) score. In their study, 25 dentists and individuals with another academic background assessed tongue photographs based on criteria such as tongue brilliance, wetness, the appearance of saliva, frothy saliva, and the presence of papillae or swelling. In contrast, our study was focused on quantitative analysis rather than visual assessment. Our goal was to achieve a more objective evaluation of salivary secretion by extracting features from various color channels and principal component images. Specifically, we extracted 462 features from tongue images and derived a relationship equation between these features and saliva secretion using machine learning, such as deep neural networks. Our proposed method may offer a more objective assessment compared to the visual approach used by [PERSON] et al., representing a significant advancement in the field. The condition of the tongue surface is correlated with the function of specific body organs [34]. Other researchers have developed tongue imaging systems and methods of analyzing tongue images. These systems utilize various technologies, such as the CCD camera [35; 36], hyper-spectral camera [37], and cell phone camera [38; 39]. Diseases such as intestinal infection, heart failure, cholecystitis, or appendicitis have been predicted or classified through tongue image analysis [40; 41]. In this paper, we present an algorithm to predict the saliva secretion from tongue images. We utilized an imaging tool specifically designed for tongue image acquisition, which included a camera and a chin support instrument. This imaging tool was used for image focusing and positioning the images. However, we encountered some image distortion due to poor user control and minor movements, leading to several unfocused and blurred images. Additionally, specific lighting conditions were not used, which may have affected the image contrast. Consequently, the image focus varied between acquisitions, complicating the extraction of distinct features from unfocused images [42]. Moreover, images obtained without adequate lighting necessitated pre-processing steps, such as normalization, to improve the quality and consistency [43]. A higher-quality dataset could potentially be obtained with fixed camera positioning and consistent lighting, reducing the need for extensive pre-processing. Moving forward, we plan to establish more controlled lighting  
Figure 8: Multi-linear regression results using neural network. conditions and refine our image acquisition protocols to ensure consistent image quality in future research. A potential limitation of this study is that the Mucus device does not measure the secretion rate but the moisture in the oral. This layer can be influenced by factors such as saliva composition and evaporation caused by mouth breathing. Nevertheless, we applied the same method consistently across all patient samples. Additionally, the distribution of measured saliva secretion was skewed, with a significant concentration of values exceeding 26 (119 of 176 samples). This imbalance in the dataset may adversely affect the regression performance for values below 26. When the training data are concentrated in a specific range in the distribution, the risk of model overfitting increases [44]. We acknowledge that a significant limitation of this study is the lack of clinical trials. If the dataset is insufficient, the model may fail to generalize with new data, leading to overfitting, where the model only learns to fit the training data. In our study, we utilized a dataset of 176 samples for regression training and achieved a high correlation between saliva secretion and tongue image features. However, we believe that the dataset size may be insufficient. We anticipate that acquiring additional data could help in developing a more generalized model.