Tooth loss is often accepted as a natural part of aging, but what if there was a way to better identify those most susceptible without the need for a dental exam? New research led by investigators at the 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 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.
Study lead investigator Hawazin Elani said: “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”.
The approach could be used to screen people globally and in a variety of healthcare settings, even by non-dental professionals, she added. In the study, the researchers used data comprising nearly 12,000 adults from the National Health and Nutrition Examination Survey to design and test five machine-learning algorithms and assess how well they predicted both complete and incremental tooth loss among adults based on socioeconomic, health, and medical characteristics.
The results of the analysis point to the importance of socioeconomic factors that shape risk beyond traditional clinical indicators.