Tooth loss is a medical problem affecting millions of people annually. It is known to have debilitating effects, both physically and psychologically. Losing one’s teeth can even affect the facial appearance, making the individual appear older than their actual age. This common condition, however, does not affect all segments of the population equally.
Scientists have been wondering if they could develop a way of predicting who is at a higher risk of developing tooth loss, in the hope that those most susceptible could be identified at an early stage when intervention is most successful. Furthermore, it has been hoped that this could even be accomplished without the need for a physical, dental examination.
Recently, the Harvard School of Dental Medicine conducted a study that utilizes machine-learning techniques to improve the identification of individuals most at risk for tooth loss. These patients could then receive a referral for testing so that they can receive dental care that could potentially slow, or halt, their tooth loss.
Researchers revealed the results of their June 18 study, which involved comparing five separate algorithms that utilized different variables to screen risk assessment for tooth loss. The study results demonstrated that algorithms that took into account traits like medical condition, socioeconomic status, race, education, and the presence of arthritis and diabetes, provided superior results than those offered by algorithms that only used dental indicators.
Researchers hope that this type of approach will be helpful in screening vulnerable adults worldwide. It is hoped that this method can be used in different types of healthcare settings, even by individuals who are not dentists or dental hygienists.
Impact of Tooth Loss
Losing teeth can be both psychologically and physically harmful to an individual. It greatly impacts their quality of life and socialization. The process of tooth loss can be delayed, or prevented, when a patient receives an early diagnosis so that they can undergo preventative dental treatments.
The reality is that many sufferers of tooth decay or gum disease delay needed dental care that could save their teeth. The type of screening tool developed by the Harvard research should be able to more precisely identify susceptible individuals so that they can receive a referral for additional assessment.
Methodology of the Study
The Harvard study followed almost 12,000 adults. The collected data was used for the development of five machine-learning algorithms that were subsequently tested upon the affected adults. The goal of the researchers was determining the ability of the algorithms to predict both incremental and complete tooth loss among those individuals within categories like medical, health and socioeconomic characteristics
Initial indications have found that the algorithms are better at the prediction of tooth loss than a basic clinical, dental indicator. The study’s researchers also found that the education level of each individual was a predictive factor, as well as employment and income status in predicting which given individual would likely suffer from tooth loss.
It has long been believed that individuals who are marginalized and have low incomes are disproportionately affected by tooth loss. The belief of the researchers is that their study will offer both dental professionals and non-dental professionals alike with a method to assess each patient’s risk factors in suffering from medical conditions that can eventually cause the loss of their teeth. This should allow susceptible individuals to receive an early diagnosis and treatment so that they can save.
DISCLAIMER: The advice offered is intended to be informational only and generic in nature. It is in no way offering a definitive diagnosis or specific treatment recommendations for your particular situation. Any advice offered is no substitute for proper evaluation and care by a qualified dentist.