Ta-Chien Chan, PhD; Ping-Chen Chung, DDS, MS; Chung-Liang Lai, MD, MPH. PHD,
Competition Sponsor: Academia Sinica of Taiwan
Oral health care is an important part of total patient care. Image recognition of gum mixing while patients chew bicolor gum establishes a novel method to test chewing efficiency, and integrates personal health information to set up a portable risk prediction platform. This easy method of measuring biting efficiency provides an alert to the risk of periodontal disease and tooth loss, especially for the elderly, and provides people with a way to prevent the risk factors for tooth loss, in turn avoiding sequential negative outcomes such as malnutrition and the need for nutrition supplements or parenteral nutrition dependency, decline in cognitive function, disability, and increased risk of cancer or death. Our hypothesis is that chewing efficiency can influence the nutrition and cognition of the elderly. To set up the prediction model of elderly nutrition and early dementia, we will collect various sources of data including masticatory function, periodontal, nutrition and cognition status. This easy-to-use approach can become a standard screening approach if a high-accuracy predictive model is established and chewing efficiency, nutrition and cognition ability can be improved through the tool’s suggestions and active care intervention by the medical team. The success of this project can not only enhance oral health among the elderly but also reduce the disease burden and improve quality of life. The promotion of this project is easy to implement worldwide, with low cost.