Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU-Net

Aim The purpose of this research was to present an artificial intelligence (AI) model, which can automatically segment and detect ectopic eruption of first permanent molars (EMMs) in early mixed dentition on panoramic radiographs using the no-new-Net (nnU-Net) model. Design A total of 438 EMMs obtained from 285 panoramic radiographs were included in this study. An AI model based on nnU-Net was trained to segment and detect EMMs. The performance of the model was evaluated by the intersection over union (IoU), precision, F1-score, accuracy and FROC. Furthermore, the detecting performance of nnU-Net was compared with that of three dentists with different years of experience using the McNemar chi-squared test. The reliability of different dentists was evaluated by intraclass correlation coefficients (ICCs). Results The nnU-Net yielded an IoU of 0.834, a precision of 0.845, an F1-score of 0.902 and an accuracy of 0.990, whereas the dentists yielded a mean IoU of 0.530, a mean precision of 0.539, a mean F1-score of 0.699 and a mean accuracy of 0.811. The ICC of different dentists was 0.776. The statistical analysis of the McNemar chi-squared test showed that the nnU-Net results were statistically significant and superior to those of dentists (p < .05). Conclusion This study validated an AI model based on nnU-Net for automatically segmenting and detecting EMMs more consistently and accurately on panoramic radiography.