Malocclusion, a prevalent dental condition worldwide, necessitates orthodontic intervention to correct tooth misalignment and improve oral health. Treatment can involve extraction of permanent teeth, depending on dental crowding, jaw relationships, and facial aesthetics. Today, clinical decision support systems have introduced machine learning (ML) to assist orthodontists in determining optimal treatment plans. This study explores the development of a novel, fully automated method for extracting dentoalveolar features from 3D intraoral scans (IOS), aiming to enhance orthodontic decision-making. Using deep learning-based IOS segmentation as basis, dental measurements were developed and utilized to train supervised ML classifiers, including support vector machines (SVM), logistic regression, decision trees, and random forests. An ensemble of SVM models demonstrated the highest accuracy (73%) in predicting extraction decisions, with these novel domain-specific features proving more informative than traditional dental arch measurements. While we can make further improvements not only in the automated segmentation but also by applying feature selection, the results highlight the potential of AI-driven analysis to streamline orthodontic workflows, reduce manual intervention and improve clinical efficiency.

Orthodontic treatment planning often involves de-ciding whether to extract teeth, a critical and irreversible decision. Integrating machine learning (ML) can enhance decision-making. This study proposes using Intraoral Scans (IOS) 3D models to predict extraction/non-extraction binary decisions with ML models. We leverage a multiview approach, using images taken from multiple points of view of the 3D model. The methodology involved a dataset composed of preprocessed IOS from 181 subjects and an experimental procedure that evaluated multiple ML models in their ability to classify subjects using either grayscale pixel intensities or radiomic features. The results indicated that a logistic model applied to the radiomic features from the back and frontal views of the 3D models was one of the best model candidates, achieving a test accuracy of 70 % and F1 score of. 73 and. 65 for non-extraction and extraction cases, respectively. Overall, these findings indicate that a multiview approach to IOS 3D models can be used to predict extraction/non-extraction decisions. In addition, the results suggest that radiomic features provide useful information in the analysis of IOS data.