J Korean Dent Sci.  2023 Jun;16(1):47-62. 10.5856/JKDS.2023.16.1.47.

New Approaches to Xerostomia with Salivary Flow Rate Based on Machine Learning Algorithm

• Lee YH
• Auh QS
• Park HK

• KMID: 2549600
• DOI: http://doi.org/10.5856/JKDS.2023.16.1.47

Abstract

Purpose
We aimed to investigate the objective cutoff values of unstimulated flow rates (UFR) and stimulated salivary flow rates (SFR) in patients with xerostomia and to present an optimal machine learning model with a classification and regression tree (CART) for all ages.
Materials and Methods
A total of 829 patients with oral diseases were enrolled (591 females; mean age, 59.29±16.40 years; 8~95 years old), 199 patients with xerostomia and 630 patients without xerostomia. Salivary and clinical characteristics were collected and analyzed. Result: Patients with xerostomia had significantly lower levels of UFR (0.29±0.22 vs. 0.41±0.24 ml/min) and SFR (1.12±0.55 vs. 1.39±0.94 ml/min) (P<0.001), respectively, compared to those with non-xerostomia. The presence of xerostomia had a significantly negative correlation with UFR (r=–0.603, P=0.002) and SFR (r=–0.301, P=0.017). In the diagnosis of xerostomia based on the CART algorithm, the presence of stomatitis, candidiasis, halitosis, psychiatric disorder, and hyperlipidemia were significant predictors for xerostomia, and the cutoff ranges for xerostomia for UFR and SFR were 0.03~0.18 ml/min and 0.85~1.6 ml/min, respectively.
Conclusion
Xerostomia was correlated with decreases in UFR and SFR, and their cutoff values varied depending on the patient’s underlying oral and systemic conditions.

Keyword

Cut-off; Decision trees; Hyposalivation; Machine learning; Saliva; Xerostomia