Multiview Machine Learning Classification of Tooth Extraction in Orthodontics Using Intraoral Scans - de Azevedo Gomes, Carlos Falcao; de Araujo, Adriel Silva; Ahmad, Sunna Imtiaz; Magnaguagno, Mauricio Cecilio; Teixeira, Vinicius Crisosthemos; Singh Rajapuri, Anushri; Roederer, Quinn; Griebler, Dalvan; Dutra, Vinicius; Turkkahraman, Hakan; Pinho, Marcio Sarroglia (Proceedings : annual International Computer Software and Applications Conference, 2024)
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.