TEXTO
PREDICTING TOOTH LOSS
Machine-learning algorithms may help identify those at risk
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 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
[5] or delay the condition.
The study 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.
Tooth loss can be physically and psychologically debilitating. It can undoubtedly affect quality of life,
[10] 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. This approach could also be used globally, in a variety of health care settings,
[15] even by non-dental professional.
“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
[20] assessing their clinical dental status.”
Indeed, it has long been known that low-income and marginalized populations experience a disproportionate
share of the burden of tooth loss, due to lack of regular access to dental care, among other reasons, and
early identification and prompt care are critical in preventing tooth loss. These new findings point to an
important new tool in achieving that and Dr. Elani and her research team shed new light on how they can
[25] most effectively target prevention efforts and improve quality of life for patients.
Adapted from sciencedaily.com. June 24, 2021. Accessed 17 September 2021.
Considering dental assessments, machine learning tools should:
