Researchers at the University of Michigan School of Dentistry in Ann Arbor recently created a machine learning algorithm that can predict a patient's risk of regenerative outcomes after peri-implantitis treatment.
The group used Fast and Robust Deconvolution of Expression Profiles, or FARDEEP, in a study to examine tissue samples of peri-implantitis patients to calculate the amount of harmful bacteria and infection-fighting immune cells of each patient. Researchers found that those who were at low risk for periodontal disease showed more immune cells.
"Much emphasis has been placed on the immune cell types that are more adept at wound healing and tissue repair," said Yu Leo Lei, DDS, PhD, senior author of the study and assistant professor of dentistry. "However, here we show that immune cell types that are central to microbial control are strongly correlated with superior clinical outcomes."
It may also be possible in the future to use the algorithm to predict the likelihood of peri-implantitis before dental implants are placed. More human clinical trials are required before the algorithm can be used widely by clinicians.