New research suggests that machine learning tools can help identify those at greatest risk for tooth loss and refer them for further dental assessment in an effort to ensure early interventions to avert or delay the condition.

New research led by investigators at Harvard School of Dental Medicine suggests that machine learning tools can help identify those at greatest risk for tooth loss and refer them for further dental assessment in an effort to ensure early interventions to avert or delay the condition.

The study, published June 18 in PLOS ONE, compared five algorithms using a different combination of variables to screen for risk. The results showed those that factored medical characteristics and socioeconomic variables, such as race, education, arthritis, and diabetes, outperformed algorithms that relied on dental clinical indicators alone.

«Our analysis showed that while all machine-learning models can be useful predictors of risk, those that incorporate socioeconomic variables can be especially powerful screening tools to identify those at heightened risk for tooth loss,» said study lead investigator Hawazin Elani, assistant professor of oral health policy and epidemiology at HSDM.

The approach could be used to screen people globally and in a variety of health care settings even by non-dental professionals, she added.

Tooth loss can be physically and psychologically debilitating. It can affect quality of life, well-being, nutrition, and social interactions. The process can be delayed, even prevented, if the earliest signs of dental disease are identified, and the condition treated promptly. Yet, many people with dental disease may not see a dentist until the process has advanced far beyond the point of saving a tooth. This is precisely where screening tools could help identify those at highest risk and refer them for further assessment, the team said.

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Materials provided by Harvard Medical School. Original written by Heather Denny. Note: Content may be edited for style and length.