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The study aimed to develop a convolutional neural network (CNN)\u2010based model to automatically detect mesiodens in cone\u2010beam computed tomography images. A datatest of anonymized 851 axial slices of 106 patients\u2019 cone\u2010beam images was used to process the artificial intelligence system for the detection and segmentation of mesiodens. The CNN model achieved high performance in mesiodens segmentation with sensitivity, precision, and F1 scores of 1, 0.9072, and 0.9513, respectively. The area under the curve (AUC) was 0.9147, indicating the model\u2019s robustness. 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