AI helps predict treatment outcomes for patients with diseased dental implants

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peri-implantitis
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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 address this, US researchers developed a machine learning algorithm, a form of artificial intelligence, to assess an individual patient’s risk of regenerative outcomes after surgical treatment of peri-implantitis.

The algorithm is called FARDEEP, which stands for Fast and Robust Deconvolution of Expression Profiles. 

In the study published in Theranosticsa team led by the University of Michigan School of Dentistry used FARDEEP to analyse tissue samples from a group of patients with peri-implantitis who were receiving reconstructive therapy. They then 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.

The team was surprised that the types of cells associated with better outcomes for implant patients challenge conventional thinking.

“Much emphasis has been placed on the immune cell types that are more adept at wound healing and tissue repair,” senior author Yu Leo Lei said. 

“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 recolonization of pathogenic bacteria and show better regenerative outcomes.”

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 is often challenging because of poor bone quality and delayed healing. Preventive implant maintenance and long-term management of peri-implantitis becomes part of the routine practice after implant reconstruction.

“Regenerative therapy for peri-implantitis is expensive and treatment outcomes are unpredictable,” first author Professor Jeff Wang said. 

“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.”

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

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