AI helps assess patient s risk of regenerative outcomes after treatment of peri-implantitis
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, a form of artificial intelligence, to assess an individual patient s risk of regenerative outcomes after surgical treatments of peri-implantitis.
The algorithm is called FARDEEP, which stands for Fast and Robust Deconvolution of Expression Profiles. In the study, 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.
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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 that end, a team led by the University of Michigan School of Dentistry developed a machine learning algorithm, a form of artificial intelligence, to assess an individual patient s risk of regenerative outcomes after surgical treatments of peri-implantitis.
The algorithm is called FARDEEP, which stands for Fast and Robust Deconvolution of Expression Profiles. In the study, 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.