2025/11/24 by Marzio Galdi, Davide Cannatà, Galdi, Marzio +11
Biochemistry, Genetics and Molecular Biology · Dentistry · #Computer Vision and Pattern Recognition (cs.CV) #Dental Radiography and Imaging #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Orthodontics and Dentofacial Orthopedics #Quantitative Methods (q-bio.QM) #dental development and anomalies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2511.20493
openalex publication_date 2025/11/24 · openalex created_date 2025/11/28 · openalex updated_date 2026/07/28
Objectives. The aim of the present study was to develop a fully deep learning model to reduce the intra- and inter-operator reproducibility of sector classification systems for predicting unerupted maxillary canine likelihood of impaction. Methods. Three orthodontists (Os) and three general dental practitioners (GDPs) classified the position of unerupted maxillary canines on 306 radiographs (T0) according to the three different sector classification systems (5-, 4-, and 3-sector classification system). The assessment was repeated after four weeks (T1). Intra- and inter-observer agreement were evaluated with Cohen's K and Fleiss K, and between group differences with a z-test. The same radiographs were tested on different artificial intelligence (AI) models, pre-trained on an extended dataset of 1,222 radiographs. The best-performing model was identified based on its sensitivity and precision. Results. The 3-sector system was found to be the classification method with highest reproducibility, with an agreement (Cohen's K values) between observations (T0 versus T1) for each examiner ranged from 0.80 to 0.92, and an overall agreement of 0.85 [95% confidence interval (CI) = 0.83-0.87]. The overall inter-observer agreement (Fleiss K) ranged from 0.69 to 0.7. The educational background did not affect either intra- or inter-observer agreement (p>0.05). DenseNet121 proved to be the best-performing model in allocating impacted canines in the three different classes, with an overall accuracy of 76.8%. Conclusion. AI models can be designed to automatically classify the position of unerupted maxillary canines.