Smart Connected Oral Health Community: Using AI and Digital Technologies to Close the Gap in Oral Health Disparity
Lead PI:
Jiebo Luo

Tooth decay is a pandemic disease that affects 35% of the global population, or 2.4 billion people. Dental caries (tooth decay) particularly impacts children and adults living in poverty, who have poor access to dental care. Current biomedical approaches to controlling dental caries have had limited success. This project is creating a smart, connected oral health community with improved access to care and greater oral health equity. The investigators aim to develop and test a community-based infrastructure that combines use of artificial intelligence (AI) technology, facilitated by home use of smartphones, with community engagement through interactive oral health community centers, mobile vans, and community health workers. The project has the potential to reform the oral health care delivery system, empower communities with digital tools, and overcome barriers to oral health equity. Beginning with a focus on families with young children, the model could also be adopted by other underserved populations, such as the elderly and refugees, who face similar challenges in accessing oral health care.

The project team is refining the underlying technology with AI-powered oral disease screening and management with cloud surveillance, and data collection facilitated by a dentistry smartphone app for at-home self-monitoring. The team is also investigating the social dimensions of the problem. It is establishing oral health community centers supported by community health workers who apply established methods of human motivation to reach and empower families in the community, and to teach and motivate them to use the new AI apps. The team will be assessing community outcomes of the project, with a focus on the use of technology tools for caries detection and treatment prioritization, and community engagement with services in oral health community centers. Outcomes will be measured by the perceived competence of providers and patients, and the technology's acceptance, usability, and effectiveness.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Jiebo Luo
Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the University of Rochester. He joined the University of Rochester in 2011 after a prolific career of 15 years at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. He has authored over 600 technical papers and more than 90 U.S. patents. He has served as program co-chair of ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017, and general co-chair of ACM Multimedia 2019 and IEEE ICME 2024, as well as on the editorial boards of several IEEE Transactions journals and publications. He served as the Editor in Chief of the IEEE Transactions on Multimedia for a 3-year term (2020-2022). He is a Fellow of NAI, ACM, AAAI, IEEE, SPIE, and IAPR.
Performance Period: 06/15/2023 - 05/31/2026
Institution: University of Rochester
Award Number: 2238208