Machine Learning Predicts Treatment Outcomes for Peri-Implantitis Patients

Dentistry Today


Peri-implantitis besets approximately a quarter of all dental implant patients, according to the University of Michigan School of Dentistry, with no reliable way to assess how patients will respond to treatment.

To that end, researchers at the school have developed a machine learning algorithm called Fast and Robust Deconvolution of Expression Profiles (FARDEEP) to assess an individual patient’s risk of regenerative outcomes after surgical treatment for peri-implantitis.

The researchers used FARDEEP to analyze 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 and assistant professor of dentistry.

The team was surprised that the types of cells associated with better outcomes for implant patients challenge conventional thinking, said Lei, who also has an appointment at the Rogel Cancer Center.

“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,” Lei said.

“Surgical management can reduce bacterial burdens across all patients. However, only the patients with more immune cell subtypes for bacterial control can suppress the recolonization of pathogenic bacteria and show better regenerative outcomes,” he said.

Dental implants have transformed reconstructive options, the researchers said, but the emerging endemic of peri-implantitis has severely compromised the long-term success of implant dentistry.

Peri-implantitis can lead to progressive bone loss, bleeding, pus, and eventual loss of the dental implants and associated crowns or dentures that they support. Replacement of a new dental implant at the previously damaged site often is challenging because of poor bone quality and delayed healing.

Preventive implant maintenance and long-term management of peri-implantitis becomes part of routine practice after implant reconstruction, the researchers said.

“Regenerative therapy for peri-implantitis is expensive, and treatment outcomes are unpredictable,” said first author Jeff Wang, clinical assistant professor and principal investigator for the regenerative treatment of peri-implantitis clinical trial.

“It would be very helpful if we could use the information to determine the best course of treatment, or maybe we’d decide that the more sensible option would be to replace an old implant with a new one, despite the challenge to rebuild the bone,” Wang said.

In the future, it may be possible to predict the risk of peri-implantitis before a dental implant is placed, Wang said. More human clinical trials are required before FARDEEP is ready to be used widely by clinicians, the researchers said.

“However, this proof of concept study offers a personalized approach to identify the types of patients that better respond to regenerative therapies,” said coauthor William Giannobile, a professor of oral medicine, infection, and immunity and dean of the Harvard School of Dental Medicine. Previously, he was at the University of Michigan School of Dentistry.

The study, “Machine Learning-Assisted Immune Profiling Stratifies Peri-Implantitis Patients With Unique Microbial Colonization and Clinical Outcomes,” was published by Theranostics.

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