New CiPD Award Advancing Artificial Intelligence in Oral Care
Philadelphia – With a new funding award, the Center for Innovation & Precision Dentistry (CiPD), Penn Dental Medicine’s collaborative center with Penn Engineering, is advancing research in the use of artificial intelligence (AI) in oral health care. Jointly sponsored by the CiPD and the Penn Institute for Biomedical Informatics (IBI) within Perelman School of Medicine, the CiPD-IBI Artificial Intelligence in Oral Health Innovation Award provides $25,000 in unrestricted funds to further develop collaborative research using AI-based approaches in oral-craniofacial health sciences. The award will be presented annually.
“The IBI is a tremendous partner with CiPD,” says Dr. Hyun (Michel) Koo, Co-Director of the CiPD. “With AI moving into healthcare applications at a blistering pace, we believe this award will help accelerate its applications for innovations in oral care from new diagnostic approaches to multiscale data integration.”
Koo explains that a key goal of this award is to develop transformative yet feasible solutions while also helping to generate preliminary data for extramural funding, the publication of the collaborative work, and the development of readily accessible AI-based tools.
The inaugural recipients of award were announced as part the CiPD’s 3rd Annual Symposium, held at Penn Dental Medicine in the summer. They include Dr. Flavia Teles, Associate Professor in the Department of Basic & Translational Sciences at Penn Dental Medicine, and Dr. Shefali Setia Verma, Assistant Professor of Pathology and Laboratory Medicine at Perelman School of Medicine, for a project titled “Advancing Periodontal Care: Harnessing AI and Comprehensive Patient Data for the Prediction of Disease Progression.” In their study, they hope to advance periodontal healthcare by harnessing AI-based approaches for integration and analyses of molecular (genetic, immunological), clinical, and demographic (gender, race, ethnicity) data aimed at predicting disease progression.
While periodontitis affects millions of adults, no methods exist to predict its initiation and progression. Dr. Teles notes that machine learning represents a novel approach to discern relationships within the clinical and molecular data that may promote disease.
“Our goal is to construct a predictive model of periodontitis progression employing Multi-Layer Perceptron (MLP) by integrating molecular, clinical, and demographic data collected before and after disease progression and readily available,” explains Dr. Teles. Their data comes from a multicenter prospective study that longitudinally followed a population of healthy individuals and periodontitis patients for 12 months, with bimonthly visits to characterize the natural progression of periodontal disease. “In our initial studies, the MLP model exhibited enhanced accuracy in comparison with traditional approaches such as logistic regression, underscoring its potential to harness multimodal data for predicting periodontal disease progression.”
Collectively, they anticipate that their findings can advance both the understanding of the etiopathogenesis of periodontitis and the development of predictive tools for risk assessment for more accurate, effective, and affordable dental care.
“The application of AI to transform health care, especially oral care, is incredibly exciting. IBI is thrilled to partner with CiPD to award new innovations and collaborations in this domain,” says Dr. Marylyn Ritchie, Director of IBI.