Reliability of non-contact tongue diagnosis for Sjögren's syndrome using machine learning method.

Sjögren's syndrome (SS) is an autoimmune disease characterized by dry mouth. The cause of SS is unknown, and its diverse symptoms make diagnosis difficult. The Saxon test, an intraoral examination, is used as the primary diagnostic method for SS, however, the risk of salivary infection is problematic. Therefore, we investigate the possibility of diagnosing SS by non-contact and imaging observation of the tongue surface. In this study, we obtained tongue photographs of 60 patients at the Tsurumi University School of Dentistry outpatient clinic to clarify the relationship between the features of the tongue and SS. We divided the tongue into four regions, and the color of each region was transformed into CIE1976L*a*b* space and statistically analyzed. To clarify experimentally the possibility of SS diagnosis using tongue color, we employed three machine-learning models: logistic regression, support vector machine, and random forest. In addition, we constructed diagnostic prediction models based on the Bagging and Stacking methods combined with three machine-learning models for comparative evaluation. This analysis used dimensionality compression by principal component analysis to eliminate redundancy in tongue color information. We found a significant difference between the a* value of the rear part of the tongue and the b* value of the middle part of the tongue in SS and non-SS patients. In addition to the principal component scores of tongue color, the support vector machine was trained using age, and achieved high accuracy (71.3%) and specificity (78.1%). The results indicate that the prediction of SS diagnosis by tongue color reaches a level comparable to machine learning models trained using the Saxon test. This is the first study using machine learning to predict SS diagnosis by non-contact tongue observation. Our proposed method can potentially support early SS detection simply and conveniently, eliminating the risk of infection at diagnosis, and it should be validated and optimized in clinical practice.

Combination Image Analysis of Tongue Color and Sublingual Vein Improves the Diagnostic Accuracy of Oketsu (Blood Stasis) in Kampo Medicine.

Aim: In tongue diagnosis, a dark purple tongue and enlarged sublingual vein are important findings of Oketsu (blood stasis). However, the association between the tongue color and the sublingual vein has not been reported. This study investigated the association between the tongue color values and the sublingual vein width using tongue image analyzing system (TIAS) for the objective assessment of blood stasis. Methods: A total of 38 patients (age 68.7 ± 11.3 years, 14 men and 24 women) who visited the Department of Kampo Medicine at Chiba University Hospital were included. Physical findings, blood test results, blood stasis score from medical records, and tongue images obtained with TIAS were analyzed. The patients were classified into two groups: patients with a sublingual vein width of ≤2.5 mm (20 patients) and those with a width of >2.5 mm (18 patients). The physical findings and the blood test results of the two groups were analyzed by Wilcoxon's rank-sum test or χ2-test, whereas logistic regression analysis was used to determine the association between the tongue color values and sublingual vein width. Receiver operating characteristic (ROC) analysis was used to differentiate blood stasis. Results: The color values significantly related to the sublingual vein width (mm) were the P1-L* and P4-L* (darkness of the tongue edge and tongue apex) and the P1-b* and P2-b* (blueness of the tongue edge and tongue posterior). The area under the curve was greater for the combination of the tongue color values and the sublingual vein width than that for either of them. Conclusion: This study demonstrated an objective evaluation of blood stasis in the tongue of patients with dark-blue discoloration and an enlarged sublingual vein. In addition, the combination of the tongue color and the sublingual vein is expected to facilitate a more reliable diagnosis of blood stasis.