Machine learning tools could help identify those at greatest risk for tooth loss and refer them for further dental assessment for early interventions to avert or delay the conditions.
The study by researchers at the Harvard School of Dental Medicine compared five different algorithms using various combinations of variables to screen for risk. The results showed those that factored medical characteristics and socioeconomic variables, including 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. This approach could be applied around the world, even allowing non-dental professionals to screen patients.
Tooth loss can affect quality of life, well-being, nutrition, and social interactions. It is also associated with dementia. If the earliest signs of dental disease are identified, then the process can be delayed or averted with prompt treatment. However, many people with dental disease may not see a dentist until the process is too far gone. This is where screening tools could help identify those at highest risk and refer them for further assessment, the team said.
For the study, the researchers used data on nearly 12 000 adults from the National Health and Nutrition Examination Survey to design and test five machine-learning algorithms and assess their predictions for both complete and incremental tooth loss among adults based on socioeconomic, health, and medical characteristics.
A key point is that algorithms were designed to assess risk without a dental exam, though anyone at risk for tooth loss would still need one. The study’s findings point to the importance of socioeconomic factors.
“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.”
Low socioeconomic status populations have long been known to have greater rates of tooth loss, likely due to lack of regular access to dental care, among other reasons, the team said.
“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 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.”
Source: Harvard Medical School
Journal information: Hawazin W. Elani et al, Predictors of tooth loss: A machine learning approach, PLOS ONE (2021). DOI: 10.1371/journal.pone.0252873
