Peri-implantitis, a condition where tissue and bone around dental implants becomes infected, besets roughly one-quarter of dental implant patients, and currently there’s no reliable way to assess how patients will respond to treatment of this condition.
To that end, a team led by the University of Michigan School of Dentistry developed a machine-learning algorithm to assess an individual patient’s risk of regenerative outcomes after surgical treatments of peri-implantitis.
The algorithm is called Fast and Robust Deconvolution of Expression Profiles (FARDEEP). In the study, researchers used FARDEEP to analyse tissue samples from a group of patients with peri-implantitis who were receiving reconstructive therapy. They quantified the abundance of harmful bacteria and certain infection-fighting immune cells in each sample.
Patients who were at low risk for periodontal disease showed more immune cells that were highly adept at controlling bacterial infections, said Yu Leo Lei, senior author.
The team was surprised that the types of cells associated with better outcomes for implant patients challenge conventional thinking, said Lei: “Much emphasis has been placed on the immune cell types that are more adept at wound healing and tissue repair. However, here we show that immune cell types that are central to microbial control are strongly correlated with superior clinical outcomes.
Surgical management can reduce bacterial burdens across all patients, however, only the patients with more immune cell subtypes for bacterial control can suppress the recolonisation of pathogenic bacteria and show better regenerative outcomes”.
In the future, it may be possible to predict the risk of peri-implantitis before a dental implant is placed, he said. More human clinical trials are required before FARDEEP is ready to be used widely by clinicians.