{"id":112729,"date":"2022-07-07T10:00:39","date_gmt":"2022-07-07T08:00:39","guid":{"rendered":"https:\/\/ziacom.com\/?p=112729"},"modified":"2022-11-29T15:00:15","modified_gmt":"2022-11-29T14:00:15","slug":"tooth-loss-could-be-predicted-thanks-to-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/ziacom.com\/en\/sector-news\/tooth-loss-could-be-predicted-thanks-to-artificial-intelligence\/","title":{"rendered":"Tooth loss could be predicted thanks to Artificial Intelligence"},"content":{"rendered":"

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The use of automatic learning algorithms that include socioeconomic data could be key to predicting edentulism.<\/span><\/span><\/h2>\n

The scientific journal <\/span>PLOS ONE<\/span><\/b>, which belongs to the <\/span>US Public Library of Science<\/span>, published last June the study <\/span>\"Predictors of tooth loss: A machine learning approach\"<\/span><\/b>, which highlights the importance of Artificial Intelligence (AI) as a tool capable of predicting tooth loss.<\/span>\u00a0<\/span><\/p>\n

The study was conducted based on the medical and socioeconomic data of nearly 12,000 American adults from the <\/span>US National Health and Nutrition Examination Survey (NHANES)<\/span><\/b> (2011-2014), a key factor in the effectiveness of the algorithms used in predicting tooth loss and functional dentition.<\/span>\u00a0<\/span><\/p>\n

As <\/span>Dr Hawazin Elani<\/span><\/b>, assistant professor of oral health policy and epidemiology at <\/span>Harvard School of Dental Medicine<\/span><\/b> and the lead author of the study, explains: \"Our analysis showed that while all machine learning models can be useful for predicting risk, <\/span>those that incorporate socioeconomic variables can be particularly powerful screening tools<\/span><\/b> for identifying people at higher risk of tooth loss\".<\/span>\u00a0<\/span><\/p>\n

The study incorporated the socio-economic factors<\/span><\/b> of patients' age, education, employment, income, race or ethnicity into the analysis, which <\/span>resulted to be more important predictors<\/span><\/b> of tooth loss than the patients' medical factors.<\/span>\u00a0<\/span><\/p>\n

The results show, <\/span>how important socio-economic levels can be for population health<\/span><\/b>. As Elani points out: \"Our results suggest that machine learning algorithm models that incorporate socioeconomic characteristics are better at predicting tooth loss than those based solely on routine clinical dental indicators.<\/span>\u00a0<\/span><\/p>\n

The <\/span>medical data analysed<\/span><\/b> included <\/span>diseases<\/span><\/b> such as arthritis, diabetes, high cholesterol, hypertension and heart disease, which were also predictive of tooth loss but <\/span>not with the results obtained with the socio-economic factors.<\/span><\/b>\u00a0<\/span><\/p>\n

For the study, <\/span>five different machine learning algorithms were analysed and developed<\/span><\/b> to<\/span><\/b> predict complete and incremental tooth loss<\/span><\/b>. For each of them, <\/span>their predictive performance was evaluated by examining the area under the receiver operating characteristics curve (AUC)<\/span><\/b>, which measures parameters of accuracy, sensitivity, specificity as well as positive results and negative predictive values.<\/span>\u00a0<\/span><\/p>\n

The AUC is a metric used to evaluate the performance of algorithms<\/span><\/b>. The<\/span> closer the AUC is to 100%, <\/span><\/b>the better the algorithm is at predicting between classes, in this case, tooth loss or no tooth loss.<\/span>\u00a0<\/span><\/p>\n

The results of the study showed that <\/span>all algorithms performed well with an AUC of over 86.5%<\/span><\/b> on average<\/span><\/b>. The <\/span>highest result was for edentulism which achieved an AUC of 89%.<\/span><\/b> Functional dentition and missing teeth<\/span><\/b> also performed very well <\/span>with an AUC of 88% and 83% respectively.<\/span><\/b>\u00a0<\/span><\/p>\n

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