# AI-driven predictive modelling of orthodontic relapse using retainer compliance and patient factors

## TL;DR

This study uses AI to predict orthodontic relapse based on retainer compliance and patient factors, improving treatment outcomes.

## Contribution

The novel contribution is an AI model using SMART microsensor data and patient variables to predict relapse with high accuracy.

## Key findings

- The Random Forest model achieved 92.3% accuracy in predicting orthodontic relapse.
- Daily retainer wear duration and treatment complexity were key predictors of relapse.
- The model supports personalized retention strategies for better post-treatment stability.

## Abstract

Orthodontic relapse remains a critical concern, often compromising long-term treatment success and patient satisfaction. Therefore,
it is of interest to develop and validate an AI-driven predictive model using SMART microsensor-based retainer compliance data and
patient-specific variables. Among 156 monitored patients over 24 months, the Random Forest algorithm achieved the highest accuracy
(92.3%), sensitivity (89.7%) and specificity (94.2%). Key predictors included daily retainer wear duration, treatment complexity, age at
completion and initial malocclusion severity. The model supports personalized retention strategies and early intervention to enhance
post-treatment stability.