Machine Learning Predicts Who Is Most at Risk of Tooth Loss

Dentistry Today
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Machine learning can help identify patients who are at the greatest risk of tooth loss so they could be referred for further assessment to avert or delay negative outcomes, according to the Harvard School of Dental Medicine (HSDM).

HSDM researchers compared five algorithms using a different combination of variables to screen for risk. The algorithms 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 lead investigator Dr. Hawazin Elani, assistant professor for oral health policy and epidemiology.

The approach could be used to screen people globally and in a variety of healthcare settings even by non-dental professionals, Elani said.

Tooth loss can be physically and psychologically debilitating, HSDM said, affecting quality of life, well-being, nutrition, and social interactions. But the process can be delayed and even prevented if the earliest signs of dental disease are identified and the condition treated promptly, HSDM continued.

However, HSDM said, many people with dental disease may not see a dentist until the process has advanced far beyond the point of saving a tooth, which is precisely where screening tools could help identify those at highest risk and refer them for further assessment.

The researchers used data from nearly 12,000 adults from the National Health and Nutrition Examination Survey to design and test the 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.

Notably, HSDM said, the algorithms were designed to assess risk without a dental exam. But anyone deemed at high risk for tooth loss would still have to undergo an actual exam, the researchers said. Also, the results of the analysis point to the importance of socioeconomic factors that shape risk beyond traditional clinical indicators.

“Our findings suggest that the machine-learning algorithm models incorporating socioeconomic characteristics were better at predicting tooth loss than those relying on routine clinical dental indicators alone,” Elani said.

“This work highlights the importance of social determinants of health. Knowing the patient’s education level, employment status, and income is just as relevant for predicting tooth loss as assessing their clinical dental status,” Elani continued.

The researchers noted that it has long been known that low-income and marginalized populations experience a disproportionate share of the burden of tooth loss, likely due to lack of regular access to dental care, among other reasons.

“As oral health professionals, we know how critical early identification and prompt care are in preventing tooth loss, and these new findings point to an important new tool in achieving that,” said Dr. Jane Barrow, associate dean for global and community health and executive director of the Initiative to Integrate Oral Health and Medicine at HSDM.

“Dr. Elani and her research team shed new light on how we can most effectively target our prevention efforts and improve quality of life for our patients,” Barrow said.

The research was done in collaboration with researchers at the Harvard TH Chan School of Public Health, the University of São Paolo in Brazil, and the University of Otago Faculty of Dentistry in New Zealand.

The study, “Predictors of Tooth Loss: A Machine Learning Approach,” was published by PLOS ONE.

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