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Developing Predictive Models for Periodontitis Progression Using Artificial Intelligence: A Longitudinal Cohort Study

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Longitudinal cohort study demonstrates machine learning models predict periodontitis progression, suggesting early detection strategies.

Predictive models can effectively forecast periodontitis progression using clinical data and biomarkers, aiding early intervention.
The probabilistic graphic model outperformed others with an AUROC of 0.88, indicating stronger predictive accuracy compared to logistic regression and multi-layer perceptron.
Logistic regression, multi-layer perceptron, and probabilistic graphic model were compared to analyze prediction capabilities within a longitudinal cohort study setting.
Key features affecting predictions included the number of deep periodontal pockets, highlighting important clinical parameters in model performance.

Furquim et al. (2025) studied this question.

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