Artificial intelligence is already reshaping diagnostics in dentistry, but researchers at UT Health San Antonio and the University of Texas at San Antonio (UTSA) are now exploring how AI could help evaluate and optimize dental composite materials.
Their goal: to develop machine learning models that can accurately predict how commercially available dental composites—used in fillings and other restorations—will perform in clinical settings.
“Very few studies provide the kind of cross-comparable data that machine learning models need,” said Kyumin Whang, Barry K. Norling Endowed Professor in Comprehensive Dentistry at UT Health San Antonio. “Even though there are thousands of papers on dental composites, the vast majority focus on new or proprietary materials tested under specific lab conditions.”
Whang and co-lead investigator Yu Shin Kim, associate professor at the UT Health San Antonio School of Dentistry, collaborated with Mario Flores, professor in electrical and computer engineering and biomedical engineering at UTSA, to build a dataset of 240 commercially available dental composites. Their work, published in the Journal of Dental Research, represents a rare cross-disciplinary effort to apply artificial intelligence to restorative dental materials.