Purpose: To develop auxiliary devices for intraoral (IO) scanning of complete-arch implants using a deep-learning AI model. Materials and Methods: A total of 338 sets of 3D imaging data were collected from a dental laboratory. Of these, 300 sets of complete dental arches were used for training, 38 sets for validation, and 10 edentulous arches with 4-6 dental implants for testing. Auxiliary devices, with landmarks placed between implants to aid in image stitching, were manually designed and used as a control. A Multi-Layer Perceptron artificial neural network was employed to predict the positions of the landmarks, using normalized implant coordinates as input and landmark coordinates as output. The model was validated and evaluated using the test set to assess the fit of the base and the surface area of the landmarks. Results: The bounding box loss for the training and validation sets converged to 0.02 and 0.01, respectively, indicating high precision in predicting landmark positions. The objectness loss stabilized at 0.05 for the training set and 0.03 for the validation set, confirming the model’s robust detection capability. The root mean square (RMS) of the device base was 0.117 ± 0.053 mm, significantly smaller than the clinical threshold of 0.300 mm (p < 0.001). The surface area of the AI-generated device landmarks (762.0 ± 141.7 mm²) was significantly smaller than that of the manually designed control (1307.1 ± 286.1 mm², p = 0.001). Conclusions: The AI model demonstrates exceptional performance in the task. The base of the AI-generated auxiliary device fits well with the edentulous region, while its landmark teeth are smaller than those of the manually designed control.
Keywords: Dental implants; intraoral scanning; edentulism; deep learning
