Mun Sae Byeol, Lim Hun Jun, Kim Young Jae, Kim Bong Chul, Kim Kwang Gi
Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, Republic of Korea.
Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, 35233, Republic of Korea.
Sci Rep. 2025 Feb 12;15(1):5202. doi: 10.1038/s41598-025-89219-2.
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.
在本研究中，我们调查了基于深度学习预测即刻种植体植入是否可行。对201例患者874颗待拔除牙齿的全景X线片（第1组：440颗拔牙后难以即刻植入种植体的牙齿，第2组：434颗拔牙后可即刻植入种植体的牙齿）进行评估，用于深度学习模型的训练和测试。使用了DenseNet121、ResNet18、ResNet101、ResNeXt101、InceptionNetV3和InceptionResNetV2。每个模型都使用预处理后的牙科数据进行训练，并将数据集分为训练集、验证集和测试集以评估模型性能。对于每个模型，灵敏度、精确率、准确率、平衡准确率和F1分数均大于0.90。本研究结果证实，基于深度学习预测即刻种植体植入可能性在相当准确的水平上是可行的。
