AI Aided Analysis on Saliva Crystallization of Pregnant Women for Accurate Estimation of Delivery Date and Fetal Status

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 5; pp. 2320 - 2330
• Main Authors: Li, Zhou-Xuan, Zha, Yue-Ming, Jiang, Guang-Yun, Huang, Yao-Xiong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI aided pattern recognition and analysis; Artificial Intelligence; Biomedical monitoring; Crystallization; Crystals; delivery date; Female; Fetus; Fetuses; Fractal analysis; Fractal geometry; Humans; Imaging; Monitoring; Pattern analysis; Predictions; Pregnancy; Pregnant Women; Proteins; Saliva; Saliva - chemistry; salivary crystallization; smartphone-based device
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2021.3135534

Saliva contains similar molecular components to serum. Analysis of saliva can provide important diagnostic information about the body. Here we report an artificial intelligence (AI) aided home-based method that can let pregnant women perform daily monitoring on their pregnant status and accurate prediction on their delivery date by the pattern analysis of their salivary crystals. The method was developed based on the information obtained from our investigation on the saliva samples of 170 pregnant women about the correlation of the salivary crystal pattern with pregnant age and fetal status. It demonstrated that the patterns of salivary crystallization could act as indicators of the pregnant age, fetal state, and some medical conditions of pregnant women. On this basis, with the aid of AI recognition and analysis of the fractal dimension and some characteristic crystals in the salivary crystallization, we performed estimation on the delivery date in both quantitative and qualitative manners. The accuracy of the prediction on 15 pregnant women was satisfactory: 100% delivering in the predicted week, 93.3% within the estimated three days, and 86.7% on the day as the prediction. We also developed a simple smartphone-based AI-aided salivary crystal imaging and analysis device as an auxiliary means to let pregnant women monitor their fetal status daily at home and predict their delivery date with adequate accuracy.