Collaborative Research: FDT-BioTech: Digital Twins with Statistical Topological Learning of Periodontal Diseases

The digital twin (DT) paradigm presents a wide array of opportunities for modeling complex systems in biomedical sciences in a realistic manner, allowing researchers and healthcare professionals to explore various “what-if” scenarios. In dental sciences, DTs can serve as virtual replicas of a patient's periodontal tissues and structures, enabling clinicians to address a variety of tasks such as simulating periodontal conditions, forecasting treatment outcomes, and personalizing dental care plans. However, achieving this vision is impossible without building confidence in making DTs in healthcare trustworthy which requires the development of novel mathematical and statistical foundations behind such fundamental questions as verification, validation, and uncertainty quantification (VVUQ) of dental DTs, robustness of dental DTs to uncertainties, and cohesive integration of multi-modal health-related data at disparate scales. This project aims to develop novel mathematical and statistical methodology to establish a foundation of the artificial intelligence (AI)-driven framework for constructing reliable and personalized DTs for periodontal health. By integrating principles from statistical learning, topological data analysis, and generative AI, specifically, probabilistic diffusion models on graphs, the project opens a pathway to build ensembles of individualized dental DTs, termed “periodontal digital siblings.” These DTs will capture patient variability and uncertainty, offering a more precise representation of individual health profiles. This inherently interdisciplinary effort bridges mathematics, statistics, machine learning, dental science, and healthcare, and promotes widely adoption of DTs in dentistry, with an ultimate goal to transform the prevention and treatment of periodontal disease through personalized, data-driven care. Additionally, this project offers a broad range of unique opportunities for interdisciplinary research training at the nexus of mathematical sciences, AI, and dental medicine, equipping the next generation of researchers with critical skills, and fostering cross-domain innovation and translational science. This project is co-funded by the Statistics Program in the Division of Mathematical Sciences. 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.