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Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional

Sung Joo Cho, Jun-Ho Moon, Dong-Yub Ko

The Angle Orthodontist

|September 4, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) models were evaluated for predicting orthodontic treatment outcomes, finding conventional methods more accurate overall. However, AI excelled in predicting soft tissue changes, suggesting a hybrid approach may be optimal.

Keywords:

Artificial intelligence Machine learning Multivariate multiple linear regression Orthodontic treatment Partial least squares Profile change

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Area of Science:

Orthodontics
Medical Imaging
Artificial Intelligence

Background:

Predicting post-orthodontic treatment changes in soft tissues and alveolar bone is crucial for treatment planning.
Conventional statistical models have limitations in capturing complex morphometric changes.

Purpose of the Study:

To assess the predictive accuracy of an AI model for soft tissue and alveolar bone changes after orthodontic treatment.
To compare the AI model's performance against traditional statistical prediction methods.

Main Methods:

1774 lateral cephalograms from 887 adult orthodontic patients were analyzed.
AI (TabNet) and conventional methods (MMLR, PLSR) were used to predict 44 hard and soft tissue landmarks.
Predictor variables included demographics, clinical data, and landmark coordinates.

Main Results:

Multivariate multiple linear regression (MMLR) showed the highest prediction accuracy.
The AI model was least accurate overall but outperformed conventional methods for 5 specific soft tissue landmarks inferior to the menton.
AI demonstrated superior prediction for soft tissue landmarks with significant variability.

Conclusions:

AI models are currently less effective than conventional statistical methods for overall prediction of orthodontic treatment changes.
AI shows promise for predicting specific soft tissue alterations.
A hybrid model combining AI and conventional methods may offer improved predictive capabilities.