(By Dr. Joseph D. Lim and Dr. Kenneth Lester Lim, BS-MMG, DDM, MSc-OI)
IN THE FUTURE, it may be possible to better identify those most susceptible to tooth loss without them going to the dentist.
New research at the Harvard School of Dental Medicine (HSDM) suggests that machine learning tools can help identify those at greatest risk for tooth loss.
The machine learning tools can then refer the patients for further dental assessment to ensure early interventions to avert or delay the condition.
Machine learning is a branch of artificial intelligence that automates the analysis of data by identifying patterns then making0 decisions with little human intervention.
The HSDM study, published June 18 in PLOS ONE, was conducted in collaboration with researchers at the Harvard T.H. 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 compared five algorithms using a different combination of variables to screen for risk.
It used data from nearly 12,000 adults from the National Health and Nutrition Examination Survey to design and test five machine-learning algorithms.
It then assessed how well the algorithms predicted both complete and incremental tooth loss among adults based on socioeconomic, health and medical characteristics.
The algorithms were designed to assess risk without a dental exam. Anyone deemed at high risk for tooth loss, however, would still have to undergo an actual examination.
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, according to a Harvard Medical School news release.
“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 Dr. 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, said Dr. Elani, the study’s lead investigator.
Tooth loss can be physically and psychologically debilitating. It can affect quality of life, well-being, nutrition and social interactions, the Harvard Medical School news release pointed out.
The process can be delayed, even prevented, if the earliest signs of dental disease are identified and the condition treated promptly, said the news release.
The problem is that many people with dental issues may not see a dentist until the process has advanced far beyond the point of saving a tooth.
This is where screening tools could help identify those at highest risk and refer them for further assessment.
“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,” Dr. Elani said.
“This work highlights the importance of social determinants of health,” she said. “Knowing the patient’s education level, employment status and income is just as relevant for predicting tooth loss as assessing their clinical dental status.”
It is no secret that low-income populations experience a disproportionate share of the burden of tooth loss. That’s because of lack of regular access to dental care, among many 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.”
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Dr. Joseph D. Lim is the former Associate Dean of the UE College of Dentistry, former Dean of the College of Dentistry, National University, past president and honorary fellow of the Asian Oral Implant Academy, and honorary fellow of the Japan College of Oral Implantologists and Honorary Life Member of Thai Association of Dental Implantology. For questions on dental health, e-mail jdlim2008@gmail.com or text 0917-8591515./PN
