Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.

In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group 2: 434 teeth possible of immediate implant placement after extraction) for extraction were evaluated for the training and testing of a deep learning model. DenseNet121, ResNet18, ResNet101, ResNeXt101, InceptionNetV3, and InceptionResNetV2 were used. Each model was trained using preprocessed dental data, and the dataset was divided into training, validation, and test sets to evaluate model performance. For each model, the sensitivity, precision, accuracy, balanced accuracy, and F1-score were all greater than 0.90. The results of this study confirm that deep-learning-based prediction of the possibility of immediate implant placement is possible at a fairly accurate level.
Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval and informed consent: This study was conducted in accordance with the guidelines of the World Medical Association Helsinki Declaration for Biomedical Research Involving Human Subjects and was approved by the public Institutional Review Board of South Korea (P01-202408-01-028). The public Institutional Review Board of South Korea waived the need for individual informed consent, either written or verbal, from the participants, owing to the non-interventional retrospective design of this study and because all data were analyzed anonymously.
(© 2025. The Author(s).)